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But in the real world, killer robots are officially known as \"autonomous weapons.\"\u003c/p>\n\u003cp>[contextly_sidebar id=\"eJw1zUjAAKCjwH7SulfcR9YdigYQG11O\"]At the Pentagon, Paul Scharre helped create the U.S. policy for such weapons. In his new book, \u003cem>Army of None: Autonomous Weapons and the Future of War, \u003c/em>Scharre discusses the state of these weapons today.\u003c/p>\n\u003cp>\"Killer robots\" might be a bit sensational, he says, but what he's talking about is a weapon that could \"go out on its own and make its own decisions about who to kill on the battlefield.\"\u003c/p>\n\u003cp>At least 30 countries have autonomous weapons that are supervised by humans for defensive purposes, Scharre says.\u003c/p>\n\u003cp>\"[These are] things that would target incoming missiles and shoot them down entirely on their own,\" he says. \"Humans are sitting there at the console that could turn it off if they need to. But in a simple way, those are autonomous weapons.\"\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>Scharre says while the current weapons are not like those seen in the movies, the technology is advancing, whether people like it or not.\u003c/p>\n\u003cp>\"Things like more advanced hobby drones, the same technology that will go into self-driving cars, all of those sensors and intelligence will make autonomous weapons also possible,\" he says.\u003c/p>\n\u003cp>[contextly_sidebar id=\"WCz51LeCnbE8x0G4ZrsNGroJshUbo3nK\"]In his book, Scharre looks at the question: \"How hard would it be for someone to build a simple, autonomous weapon in their garage?\"\u003c/p>\n\u003cp>And while that's a scary scenario, he says that it's already happening on some levels as students today are learning programming skills, with free and readily available online resources.\u003c/p>\n\u003cp>\"These tools are available for free download. You can download them online,\" he says. \"[It] took me about three minutes online to find all of the free tools you would need to download this technology and make it happen.\"\u003c/p>\n\u003cp>And while high school students aren't creating these autonomous weapons, the ability to do so is a real possibility. Because of that, Scharre says the debate isn't so much about if this type of technology should be created, but more so what should be done about it.\u003c/p>\n\u003cp>\"What do we do with this? Do we build weaponized versions of them? Do you build them en masse? Do militaries invest in this?\" are all questions being asked as this technology would drastically change warfare.[contextly_sidebar id=\"kEIX8BCTWt9GXcGk8DTx7MsEwX0vyuig\"]\u003c/p>\n\u003cp>\"[It would create] a domain of warfare where humans have less control over what happens on the battlefield — where humans are no longer deciding who lives and who dies, and machines are making those decisions,\" Scharre says.\u003c/p>\n\u003cp>Debates like this are happening in countries all around the world, including those that have repeatedly violated international rules.\u003c/p>\n\u003cp>In Russia, the military is working to create a fleet of \u003ca href=\"http://nationalinterest.org/blog/the-buzz/russia-building-army-robots-24969\" target=\"_blank\" rel=\"noopener\">armed ground robots.\u003c/a>\u003c/p>\n\u003cp>\"They're building large, ground combat vehicles that have anti-tank missiles on them,\" Scharre says. \"Russian generals have talked about a vision in the future of fully robotized units that are independently conducting operations, so other countries are leaning hard into this technology.\"\u003c/p>\n\u003cp>Scharre says that one of the fears of this technology advancing is that \"flash wars\" could occur. Much like a \"flash crash\" in the stock market, a \"flash war\" would occur at such as fast pace that humans would not be involved.\u003c/p>\n\u003cp>[contextly_sidebar id=\"jrjhwoGs48lNnv3YJd09gywhycA0hUD0\"]\"The worry is that you get an equivalent — a flash war, where algorithms interact in some way and the robots start shooting each other and running amuck, and then humans are scrambling to put a lid back on it,\" Scharre says.\u003c/p>\n\u003cp>But, though some scenarios are terrifying, other people argue that autonomous weapons could save lives, by making fewer mistakes than might result from human error.\u003c/p>\n\u003cp>\"Just like self-driving cars could someday make the roads much safer, some people have argued, 'Well, maybe autonomous weapons could be more precise and more humane. By avoiding civilian casualties in war and only killing the enemy,' \" Scharre says.\u003c/p>\n\u003cp>From his own experience in the military serving as a special operations agent, Scharre says he has been in a situation in which an autonomous weapon would have killed a girl that the Taliban was using as a scout, but that soldiers did not target.\u003c/p>\n\u003cp>He says it's situations like that which highlight differences between what is legal in the laws of war and what is morally right — something that autonomous weapons might not distinguish.\u003c/p>\n\u003cp>\"That is one of the concerns that people raise about autonomous weapons is a lack of an ability to feel empathy and to engage in mercy in war,\" Scharre says. \"And that if we built these weapons, they would take away a powerful restraint in warfare that humans have.\"\u003c/p>\n\u003cp>There's still a lot to consider and discuss when it comes to autonomous weapons and the increasing technology, Scharre says. But as for whether humans are doomed, he says there is not a clear answer.\u003c/p>\n\u003cp>\"We do have the opportunity to shape how we use technology. We're not at the mercy of it,\" Schare says. \"The problem at the end of the day isn't the technology. It's getting humans to cooperate together on how we use the technology and make sure that we're using it for good and not for harm.\"\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>Noah Caldwell and Emily Kopp produced and edited the audio for this story. Wynne Davis adapted it for Web. \u003c/em>\u003c/p>\n\u003cdiv class=\"fullattribution\">Copyright 2018 NPR. To see more, visit http://www.npr.org/.\u003cimg src=\"https://www.google-analytics.com/__utm.gif?utmac=UA-5828686-4&utmdt=Autonomous+Weapons+Would+Take+Warfare+To+A+New+Domain%2C+Without+Humans&utme=8(APIKey)9(MDAxOTAwOTE4MDEyMTkxMDAzNjczZDljZA004)\">\u003c/div>\n\n","blocks":[],"excerpt":"Former special operations agent Paul Scharre helped create U.S. military guidelines on autonomous weapons. His new book \u003cem>Army of None,\u003c/em> looks at the advances in technology, and the questions they raise.","status":"publish","parent":0,"modified":1524765041,"stats":{"hasAudio":false,"hasVideo":false,"hasChartOrMap":false,"iframeSrcs":[],"hasGoogleForm":false,"hasGallery":false,"hasHearkenModule":false,"hasPolis":false,"paragraphCount":28,"wordCount":927},"headData":{"title":"Autonomous Weapons May One Day Trigger Robot Warfare | KQED","description":"Former special operations agent Paul Scharre helped create U.S. military guidelines on autonomous weapons. 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But in the real world, killer robots are officially known as \"autonomous weapons.\"\u003c/p>\n\u003cp>\u003c/p>\u003cp>\u003c/p>\u003cp>At the Pentagon, Paul Scharre helped create the U.S. policy for such weapons. In his new book, \u003cem>Army of None: Autonomous Weapons and the Future of War, \u003c/em>Scharre discusses the state of these weapons today.\u003c/p>\n\u003cp>\"Killer robots\" might be a bit sensational, he says, but what he's talking about is a weapon that could \"go out on its own and make its own decisions about who to kill on the battlefield.\"\u003c/p>\n\u003cp>At least 30 countries have autonomous weapons that are supervised by humans for defensive purposes, Scharre says.\u003c/p>\n\u003cp>\"[These are] things that would target incoming missiles and shoot them down entirely on their own,\" he says. \"Humans are sitting there at the console that could turn it off if they need to. But in a simple way, those are autonomous weapons.\"\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"fullwidth"},"numeric":["fullwidth"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>Scharre says while the current weapons are not like those seen in the movies, the technology is advancing, whether people like it or not.\u003c/p>\n\u003cp>\"Things like more advanced hobby drones, the same technology that will go into self-driving cars, all of those sensors and intelligence will make autonomous weapons also possible,\" he says.\u003c/p>\n\u003cp>\u003c/p>\u003cp>\u003c/p>\u003cp>In his book, Scharre looks at the question: \"How hard would it be for someone to build a simple, autonomous weapon in their garage?\"\u003c/p>\n\u003cp>And while that's a scary scenario, he says that it's already happening on some levels as students today are learning programming skills, with free and readily available online resources.\u003c/p>\n\u003cp>\"These tools are available for free download. You can download them online,\" he says. \"[It] took me about three minutes online to find all of the free tools you would need to download this technology and make it happen.\"\u003c/p>\n\u003cp>And while high school students aren't creating these autonomous weapons, the ability to do so is a real possibility. Because of that, Scharre says the debate isn't so much about if this type of technology should be created, but more so what should be done about it.\u003c/p>\n\u003cp>\"What do we do with this? Do we build weaponized versions of them? Do you build them en masse? Do militaries invest in this?\" are all questions being asked as this technology would drastically change warfare.\u003c/p>\u003cp>\u003c/p>\u003cp>\u003c/p>\n\u003cp>\"[It would create] a domain of warfare where humans have less control over what happens on the battlefield — where humans are no longer deciding who lives and who dies, and machines are making those decisions,\" Scharre says.\u003c/p>\n\u003cp>Debates like this are happening in countries all around the world, including those that have repeatedly violated international rules.\u003c/p>\n\u003cp>In Russia, the military is working to create a fleet of \u003ca href=\"http://nationalinterest.org/blog/the-buzz/russia-building-army-robots-24969\" target=\"_blank\" rel=\"noopener\">armed ground robots.\u003c/a>\u003c/p>\n\u003cp>\"They're building large, ground combat vehicles that have anti-tank missiles on them,\" Scharre says. \"Russian generals have talked about a vision in the future of fully robotized units that are independently conducting operations, so other countries are leaning hard into this technology.\"\u003c/p>\n\u003cp>Scharre says that one of the fears of this technology advancing is that \"flash wars\" could occur. Much like a \"flash crash\" in the stock market, a \"flash war\" would occur at such as fast pace that humans would not be involved.\u003c/p>\n\u003cp>\u003c/p>\u003cp>\u003c/p>\u003cp>\"The worry is that you get an equivalent — a flash war, where algorithms interact in some way and the robots start shooting each other and running amuck, and then humans are scrambling to put a lid back on it,\" Scharre says.\u003c/p>\n\u003cp>But, though some scenarios are terrifying, other people argue that autonomous weapons could save lives, by making fewer mistakes than might result from human error.\u003c/p>\n\u003cp>\"Just like self-driving cars could someday make the roads much safer, some people have argued, 'Well, maybe autonomous weapons could be more precise and more humane. By avoiding civilian casualties in war and only killing the enemy,' \" Scharre says.\u003c/p>\n\u003cp>From his own experience in the military serving as a special operations agent, Scharre says he has been in a situation in which an autonomous weapon would have killed a girl that the Taliban was using as a scout, but that soldiers did not target.\u003c/p>\n\u003cp>He says it's situations like that which highlight differences between what is legal in the laws of war and what is morally right — something that autonomous weapons might not distinguish.\u003c/p>\n\u003cp>\"That is one of the concerns that people raise about autonomous weapons is a lack of an ability to feel empathy and to engage in mercy in war,\" Scharre says. \"And that if we built these weapons, they would take away a powerful restraint in warfare that humans have.\"\u003c/p>\n\u003cp>There's still a lot to consider and discuss when it comes to autonomous weapons and the increasing technology, Scharre says. But as for whether humans are doomed, he says there is not a clear answer.\u003c/p>\n\u003cp>\"We do have the opportunity to shape how we use technology. We're not at the mercy of it,\" Schare says. \"The problem at the end of the day isn't the technology. It's getting humans to cooperate together on how we use the technology and make sure that we're using it for good and not for harm.\"\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>Noah Caldwell and Emily Kopp produced and edited the audio for this story. Wynne Davis adapted it for Web. \u003c/em>\u003c/p>\n\u003cdiv class=\"fullattribution\">Copyright 2018 NPR. To see more, visit http://www.npr.org/.\u003cimg src=\"https://www.google-analytics.com/__utm.gif?utmac=UA-5828686-4&utmdt=Autonomous+Weapons+Would+Take+Warfare+To+A+New+Domain%2C+Without+Humans&utme=8(APIKey)9(MDAxOTAwOTE4MDEyMTkxMDAzNjczZDljZA004)\">\u003c/div>\n\n\u003c/div>\u003c/p>","attributes":{"named":{},"numeric":[]}}],"link":"/futureofyou/441008/autonomous-weapons-may-one-day-trigger-robot-warfare","authors":["byline_futureofyou_441008"],"programs":["futureofyou_54"],"categories":["futureofyou_1","futureofyou_73"],"tags":["futureofyou_849","futureofyou_1491","futureofyou_365","futureofyou_35","futureofyou_1490"],"featImg":"futureofyou_441009","label":"source_futureofyou_441008"},"futureofyou_440231":{"type":"posts","id":"futureofyou_440231","meta":{"index":"posts_1591205157","site":"futureofyou","id":"440231","score":null,"sort":[1521225109000]},"guestAuthors":[],"slug":"can-computers-learn-like-humans","title":"Can Computers Learn Like Humans?","publishDate":1521225109,"format":"standard","headTitle":"KQED Future of You | KQED Science","labelTerm":{},"content":"\u003cp>The world of artificial intelligence has exploded in recent years. Computers armed with AI do everything from drive cars to pick movies you'll probably like. Some have warned we're putting too much trust in computers that appear to do wondrous things.\u003c/p>\n\u003cp>But what exactly do people mean when they talk about artificial intelligence?\u003c/p>\n\u003cp>It's hard to find a universally accepted \u003ca href=\"https://ai100.stanford.edu/2016-report/section-i-what-artificial-intelligence/defining-ai\" target=\"_blank\" rel=\"noopener\">definition\u003c/a> of artificial intelligence. Basically it's about getting a computer to be smart — getting it to do something that in the past only humans could do.\u003c/p>\n\u003cp>One key to artificial intelligence is machine learning. Instead of telling a computer how to do something, you write a program that lets the computer figure out how to do something all on its own.\u003c/p>\n\u003cp>For example, let's say you'd like a computer to be able to pick out one conversation in a crowded restaurant.\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>\"This is a problem that's been around for a long time,\" says computer scientist John Hershey, now at Google. \"When people start to lose their hearing this is one of the first things to go — the ability to separate one voice from another.\"\u003c/p>\n\u003cp>Hershey says nobody knows how our brains are able to separate voices, so it's difficult to tell a computer how to do it. But when he worked at the \u003ca href=\"http://www.merl.com/\" target=\"_blank\" rel=\"noopener\">Mitsubishi Electric Research Laboratory\u003c/a> in Cambridge, Mass., Hershey and his colleague Jonathan Le Roux used a technique called deep learning that allowed a computer, over time, to learn how to separate voices.[contextly_sidebar id=\"aSSAepQzkEyDhphKPsyhusdnA5B9qugK\"]\u003c/p>\n\u003cp>\u003ca href=\"https://www.npr.org/sections/alltechconsidered/2014/02/20/280232074/deep-learning-teaching-computers-to-tell-things-apart\">Deep learning\u003c/a> is all the rage in AI these days. It works something like this. You give the computer some input, in this case, the sound of people talking. To the computer, this is at first just meaningless noise. But then you give the computer a transcript of what the people were saying.\u003c/p>\n\u003cp>Like a baby learning new words, the computer figures out what sounds go with what words.\u003c/p>\n\u003cp>Once it has practiced, and practiced, and practiced, it can apply what it has learned to voices it's never heard before.\u003c/p>\n\u003cp>Hershey invited me to send him voices to see if his program could separate them from each other. I sent him a 10-second clip of NPR's Kelly McEvers and Ari Shapiro speaking simultaneously.\u003c/p>\n\u003cp>Here's what I sent.\u003c/p>\n\u003cp>\u003c!-- iframe plugin v.4.3 wordpress.org/plugins/iframe/ -->\u003cbr>\n\u003ciframe src=\"https://www.npr.org/player/embed/583321707/583325732\" width=\"100%\" height=\"290\" frameborder=\"0\" scrolling=\"no\" title=\"NPR embedded audio player\" class=\"iframe-class\">\u003c/iframe>\u003c/p>\n\u003cp>And here's what the computer came up with.\u003c/p>\n\u003cp>\u003c!-- iframe plugin v.4.3 wordpress.org/plugins/iframe/ -->\u003cbr>\n\u003ciframe src=\"https://www.npr.org/player/embed/583321707/583333152\" width=\"100%\" height=\"290\" frameborder=\"0\" scrolling=\"no\" title=\"NPR embedded audio player\" class=\"iframe-class\">\u003c/iframe>\u003c/p>\n\u003cp>In the separated recording you can clearly hear what Kelly is saying, but there's still a bit of Ari's voice in the background.\u003c/p>\n\u003cp>Hershey says the computer's flubs may be because Kelly's and Ari's voices are quite different from the voices used in the computer's training.\u003c/p>\n\u003cp>And that's a problem, according to critics of the deep learning approach.\u003c/p>\n\u003cp>\"You still need a lot of data to make this technique work,\" says New York University psychologist \u003ca href=\"http://www.psych.nyu.edu/gary/\" target=\"_blank\" rel=\"noopener\">Gary Marcus\u003c/a>, who works on artificial intelligence.\u003c/p>\n\u003cp>Deep learning in computers resembles how scientists think the human brain works. The brain is made up of about 100 billion or so neurons. Researchers say the connections among these neurons change as people learn a new task. Something similar is going on inside a computer.\u003c/p>\n\u003cp>But human brains learn a lot of stuff on their own. Marcus says for deep learning, you need a lot of data to train the computer.\u003c/p>\n\u003cp>\"And sometimes you can't find that data,\" he says.\u003c/p>\n\u003cp>Marcus worries people may be too enthralled with this approach to see its limitations.\u003c/p>\n\u003cp>\"One of the key questions right now is how risky is it if I make a crazy error?\" he says.\u003c/p>\n\u003cp>So let's say the computer in a driver-less car sees someone wearing a T-shirt with a picture on it of a highway receding into the distance. It's just possible the computer would be misled that the road on the shirt was a real road.\u003c/p>\n\u003cp>\"They make a mistake. They're not perfect. And the question is how much does that cost you?\" Marcus says.\u003c/p>\n\u003cp>He notes that if you're using artificial intelligence to pick a song people might like, an error is hardly catastrophic. \"But if you made a pedestrian detector that's 99 percent correct that sounds good,\" he says, \"... then you do the math and think about how many people would die every day if you had a fleet of those cars and it's really not very good at all.\"\u003c/p>\n\u003cp>But computer scientist \u003ca href=\"http://www.astroteller.net/\" target=\"_blank\" rel=\"noopener\">Astro Teller\u003c/a> is more positive about what AI can do. He heads a Google spinoff company \u003ca href=\"https://x.company/\" target=\"_blank\" rel=\"noopener\">called X\u003c/a>. First of all, he says, car-driving computers are already getting it right more than 99 percent of the time. He says even if they don't \u003cem>always\u003c/em> get it right, cars equipped with AI computers are likely to do way better than humans in unexpected situations.\u003c/p>\n\u003cp>But he also believes deep learning has its limits.\u003c/p>\n\u003cp>\"Most people in the field of artificial intelligence are excited about deep learning, and the progress that it's making but I think very few of them think that it's going to be the whole nut,\" Teller says.\u003c/p>\n\u003cp>He's convinced that researchers will come up with new AI techniques that will make computers much smarter than they are today.\u003c/p>\n\u003cp>\"I don't think there are any inherent limits in the kinds of problems computers can solve,\" Teller says. \"And I hope that there aren't any limits.\"\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>Of course, having no limits poses its own set of interesting questions.\u003c/p>\n\u003chr>\n\u003cdiv class=\"fullattribution\">Copyright 2018 NPR. To see more, visit http://www.npr.org/.\u003cimg src=\"https://www.google-analytics.com/__utm.gif?utmac=UA-5828686-4&utmdt=Can+Computers+Learn+Like+Humans%3F&utme=8(APIKey)9(MDAxOTAwOTE4MDEyMTkxMDAzNjczZDljZA004)\">\u003c/div>\n\n","blocks":[],"excerpt":"What exactly is artificial intelligence? How does it work? What are its limits?","status":"publish","parent":0,"modified":1521225363,"stats":{"hasAudio":false,"hasVideo":false,"hasChartOrMap":false,"iframeSrcs":[],"hasGoogleForm":false,"hasGallery":false,"hasHearkenModule":false,"hasPolis":false,"paragraphCount":35,"wordCount":932},"headData":{"title":"Can Computers Learn Like Humans? | KQED","description":"What exactly is artificial intelligence? How does it work? What are its limits?","ogTitle":"","ogDescription":"","ogImgId":"","twTitle":"","twDescription":"","twImgId":"","schema":{"@context":"http://schema.org","@type":"Article","headline":"Can Computers Learn Like Humans?","datePublished":"2018-03-16T18:31:49.000Z","dateModified":"2018-03-16T18:36:03.000Z","image":"https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"}},"disqusIdentifier":"440231 https://ww2.kqed.org/futureofyou/?p=440231","disqusUrl":"https://ww2.kqed.org/futureofyou/2018/03/16/can-computers-learn-like-humans/","disqusTitle":"Can Computers Learn Like Humans?","source":"Your Brain On Tech","nprByline":"Joe Palca\u003cbr />NPR All Tech Considered","nprImageAgency":"Sam Rowe for NPR","nprStoryId":"583321707","nprApiLink":"http://api.npr.org/query?id=583321707&apiKey=MDAxOTAwOTE4MDEyMTkxMDAzNjczZDljZA004","nprHtmlLink":"https://www.npr.org/sections/alltechconsidered/2018/02/05/583321707/can-computers-learn-like-humans?ft=nprml&f=583321707","nprRetrievedStory":"1","nprPubDate":"Mon, 05 Feb 2018 21:56:00 -0500","nprStoryDate":"Mon, 05 Feb 2018 14:46:00 -0500","nprLastModifiedDate":"Mon, 19 Feb 2018 07:27:42 -0500","nprAudio":"https://ondemand.npr.org/anon.npr-mp3/npr/atc/2018/02/20180205_atc_can_computers_learn_like_humans.mp3?orgId=1&topicId=1007&aggIds=156490415&d=289&p=2&story=583321707&ft=nprml&f=583321707","nprAudioM3u":"http://api.npr.org/m3u/1583461897-8f4ffb.m3u?orgId=1&topicId=1007&aggIds=156490415&d=289&p=2&story=583321707&ft=nprml&f=583321707","path":"/futureofyou/440231/can-computers-learn-like-humans","audioUrl":"https://ondemand.npr.org/anon.npr-mp3/npr/atc/2018/02/20180205_atc_can_computers_learn_like_humans.mp3?orgId=1&topicId=1007&aggIds=156490415&d=289&p=2&story=583321707&ft=nprml&f=583321707","audioTrackLength":null,"parsedContent":[{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003cp>The world of artificial intelligence has exploded in recent years. Computers armed with AI do everything from drive cars to pick movies you'll probably like. Some have warned we're putting too much trust in computers that appear to do wondrous things.\u003c/p>\n\u003cp>But what exactly do people mean when they talk about artificial intelligence?\u003c/p>\n\u003cp>It's hard to find a universally accepted \u003ca href=\"https://ai100.stanford.edu/2016-report/section-i-what-artificial-intelligence/defining-ai\" target=\"_blank\" rel=\"noopener\">definition\u003c/a> of artificial intelligence. Basically it's about getting a computer to be smart — getting it to do something that in the past only humans could do.\u003c/p>\n\u003cp>One key to artificial intelligence is machine learning. Instead of telling a computer how to do something, you write a program that lets the computer figure out how to do something all on its own.\u003c/p>\n\u003cp>For example, let's say you'd like a computer to be able to pick out one conversation in a crowded restaurant.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"fullwidth"},"numeric":["fullwidth"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>\"This is a problem that's been around for a long time,\" says computer scientist John Hershey, now at Google. \"When people start to lose their hearing this is one of the first things to go — the ability to separate one voice from another.\"\u003c/p>\n\u003cp>Hershey says nobody knows how our brains are able to separate voices, so it's difficult to tell a computer how to do it. But when he worked at the \u003ca href=\"http://www.merl.com/\" target=\"_blank\" rel=\"noopener\">Mitsubishi Electric Research Laboratory\u003c/a> in Cambridge, Mass., Hershey and his colleague Jonathan Le Roux used a technique called deep learning that allowed a computer, over time, to learn how to separate voices.\u003c/p>\u003cp>\u003c/p>\u003cp>\u003c/p>\n\u003cp>\u003ca href=\"https://www.npr.org/sections/alltechconsidered/2014/02/20/280232074/deep-learning-teaching-computers-to-tell-things-apart\">Deep learning\u003c/a> is all the rage in AI these days. It works something like this. You give the computer some input, in this case, the sound of people talking. To the computer, this is at first just meaningless noise. But then you give the computer a transcript of what the people were saying.\u003c/p>\n\u003cp>Like a baby learning new words, the computer figures out what sounds go with what words.\u003c/p>\n\u003cp>Once it has practiced, and practiced, and practiced, it can apply what it has learned to voices it's never heard before.\u003c/p>\n\u003cp>Hershey invited me to send him voices to see if his program could separate them from each other. I sent him a 10-second clip of NPR's Kelly McEvers and Ari Shapiro speaking simultaneously.\u003c/p>\n\u003cp>Here's what I sent.\u003c/p>\n\u003cp>\u003c!-- iframe plugin v.4.3 wordpress.org/plugins/iframe/ -->\u003cbr>\n\u003ciframe src=\"https://www.npr.org/player/embed/583321707/583325732\" width=\"100%\" height=\"290\" frameborder=\"0\" scrolling=\"no\" title=\"NPR embedded audio player\" class=\"iframe-class\">\u003c/iframe>\u003c/p>\n\u003cp>And here's what the computer came up with.\u003c/p>\n\u003cp>\u003c!-- iframe plugin v.4.3 wordpress.org/plugins/iframe/ -->\u003cbr>\n\u003ciframe src=\"https://www.npr.org/player/embed/583321707/583333152\" width=\"100%\" height=\"290\" frameborder=\"0\" scrolling=\"no\" title=\"NPR embedded audio player\" class=\"iframe-class\">\u003c/iframe>\u003c/p>\n\u003cp>In the separated recording you can clearly hear what Kelly is saying, but there's still a bit of Ari's voice in the background.\u003c/p>\n\u003cp>Hershey says the computer's flubs may be because Kelly's and Ari's voices are quite different from the voices used in the computer's training.\u003c/p>\n\u003cp>And that's a problem, according to critics of the deep learning approach.\u003c/p>\n\u003cp>\"You still need a lot of data to make this technique work,\" says New York University psychologist \u003ca href=\"http://www.psych.nyu.edu/gary/\" target=\"_blank\" rel=\"noopener\">Gary Marcus\u003c/a>, who works on artificial intelligence.\u003c/p>\n\u003cp>Deep learning in computers resembles how scientists think the human brain works. The brain is made up of about 100 billion or so neurons. Researchers say the connections among these neurons change as people learn a new task. Something similar is going on inside a computer.\u003c/p>\n\u003cp>But human brains learn a lot of stuff on their own. Marcus says for deep learning, you need a lot of data to train the computer.\u003c/p>\n\u003cp>\"And sometimes you can't find that data,\" he says.\u003c/p>\n\u003cp>Marcus worries people may be too enthralled with this approach to see its limitations.\u003c/p>\n\u003cp>\"One of the key questions right now is how risky is it if I make a crazy error?\" he says.\u003c/p>\n\u003cp>So let's say the computer in a driver-less car sees someone wearing a T-shirt with a picture on it of a highway receding into the distance. It's just possible the computer would be misled that the road on the shirt was a real road.\u003c/p>\n\u003cp>\"They make a mistake. They're not perfect. And the question is how much does that cost you?\" Marcus says.\u003c/p>\n\u003cp>He notes that if you're using artificial intelligence to pick a song people might like, an error is hardly catastrophic. \"But if you made a pedestrian detector that's 99 percent correct that sounds good,\" he says, \"... then you do the math and think about how many people would die every day if you had a fleet of those cars and it's really not very good at all.\"\u003c/p>\n\u003cp>But computer scientist \u003ca href=\"http://www.astroteller.net/\" target=\"_blank\" rel=\"noopener\">Astro Teller\u003c/a> is more positive about what AI can do. He heads a Google spinoff company \u003ca href=\"https://x.company/\" target=\"_blank\" rel=\"noopener\">called X\u003c/a>. First of all, he says, car-driving computers are already getting it right more than 99 percent of the time. He says even if they don't \u003cem>always\u003c/em> get it right, cars equipped with AI computers are likely to do way better than humans in unexpected situations.\u003c/p>\n\u003cp>But he also believes deep learning has its limits.\u003c/p>\n\u003cp>\"Most people in the field of artificial intelligence are excited about deep learning, and the progress that it's making but I think very few of them think that it's going to be the whole nut,\" Teller says.\u003c/p>\n\u003cp>He's convinced that researchers will come up with new AI techniques that will make computers much smarter than they are today.\u003c/p>\n\u003cp>\"I don't think there are any inherent limits in the kinds of problems computers can solve,\" Teller says. \"And I hope that there aren't any limits.\"\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>Of course, having no limits poses its own set of interesting questions.\u003c/p>\n\u003chr>\n\u003cdiv class=\"fullattribution\">Copyright 2018 NPR. To see more, visit http://www.npr.org/.\u003cimg src=\"https://www.google-analytics.com/__utm.gif?utmac=UA-5828686-4&utmdt=Can+Computers+Learn+Like+Humans%3F&utme=8(APIKey)9(MDAxOTAwOTE4MDEyMTkxMDAzNjczZDljZA004)\">\u003c/div>\n\n\u003c/div>\u003c/p>","attributes":{"named":{},"numeric":[]}}],"link":"/futureofyou/440231/can-computers-learn-like-humans","authors":["byline_futureofyou_440231"],"categories":["futureofyou_1062","futureofyou_1","futureofyou_1061"],"tags":["futureofyou_849","futureofyou_56","futureofyou_905","futureofyou_131"],"featImg":"futureofyou_440232","label":"source_futureofyou_440231"},"futureofyou_439513":{"type":"posts","id":"futureofyou_439513","meta":{"index":"posts_1591205157","site":"futureofyou","id":"439513","score":null,"sort":[1518734029000]},"guestAuthors":[],"slug":"your-kids-will-still-have-jobs-despite-artificial-intelligence-commentary","title":"There Will Be Jobs for Your Kids, Despite Artificial Intelligence (Commentary)","publishDate":1518734029,"format":"standard","headTitle":"KQED Future of You | KQED Science","labelTerm":{},"content":"\u003cp>\"Whatever your job is the chances are that one of these machines can do it faster or better than you can.\"\u003c/p>\n\u003cp>No, this is not a 2018 headline about self-driving cars or one of IBM’s new supercomputers. Instead, it was published by the \u003ca href=\"https://issuu.com/bloomsburypublishing/docs/electronic_dreams_extract\" target=\"_blank\" rel=\"noopener\">\u003cem>Daily Mirror\u003c/em> in 1955\u003c/a>, when a computer took as much space as a large kitchen and had less power than a pocket calculator. They were called “electronic brains” back then, and evoked both hope and fear. And more than 20 years later, little had changed: In a \u003ca href=\"http://podplayer.net/#/?id=9183280\" target=\"_blank\" rel=\"noopener\">1978 BBC documentary\u003c/a> about silicon chips, one commentator argued that “They are the reason why Japan is abandoning its shipbuilding and why our children will grow up without jobs to go to.\"\u003c/p>\n\u003ch2>Artificial intelligence hype is not new\u003c/h2>\n\u003cp>If one types “artificial intelligence” (AI) on Google Books’ Ngram Viewer – a tool that allows us to check how often a term was printed in a book between 1800 and 2008 – we can \u003ca href=\"https://books.google.com/ngrams/graph?content=artificial+intelligence\" target=\"_blank\" rel=\"noopener\">clearly see\u003c/a> that our modern-day hype, optimism and deep concern about AI are by no means a novelty.\u003c/p>\n\u003cp>\u003cimg class=\"aligncenter\" src=\"https://images.theconversation.com/files/206209/original/file-20180213-44630-8tobd.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip\" alt=\"\" width=\"754\" height=\"333\">\u003c/p>\n\u003cp>The history of AI is a long series of booms and busts. The first “AI spring” took place between 1956 and 1974, with pioneers such as the young \u003ca href=\"https://web.media.mit.edu/%7Eminsky/\" target=\"_blank\" rel=\"noopener\">Marvin Minsky\u003c/a>. This was followed by the “first AI winter” (1974-1980), when disillusion with the gap between \u003ca href=\"https://theconversation.com/what-is-machine-learning-76759\" target=\"_blank\" rel=\"noopener\">machine learning\u003c/a> and human cognitive capacities first led to disinvestment and disinterest in the topic. A second boom (1980-1987) was followed by another “winter” (1987-2001). Since the 2000s we’ve been surfing the third “AI spring”.\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>There’s plenty of reasons to believe this latest wave of interest for AI is going to be more durable. \u003ca href=\"https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/\" target=\"_blank\" rel=\"noopener\">According to Gartner Research\u003c/a>, technologies typically go from a “peak of inflated expectations” through a “trough of disillusionment” until they finally reach a “plateau of productivity”. AI-intensive technologies such as virtual assistants, the Internet of Things, smart robots and augmented data discovery are about to reach the peak. \u003ca href=\"https://theconversation.com/deep-learning-and-neural-networks-77259\" target=\"_blank\" rel=\"noopener\">Deep learning\u003c/a>, machine learning and cognitive expert advisors are expected to reach the plateau of mainstream applications in two to five years.\u003c/p>\n\u003ch2>Narrow intelligence\u003c/h2>\n\u003cp>We finally seem to have enough computing power to credibly develop what is called “narrow AI”, of which all the aforementioned technologies are an example. These are not to be confused with “artificial general intelligence” (AGI), which scientist and futurologist Ray Kurzweil called \u003ca href=\"http://www.kurzweilai.net/are-we-spiritual-machines-ray-kurzweil-critics-strong-ai\" target=\"_blank\" rel=\"noopener\">“strong AI.”\u003c/a> Some of the most advanced AI systems to date, such as IBM’s Watson supercomputer or \u003ca href=\"https://www.theguardian.com/technology/2017/may/23/alphago-google-ai-beats-ke-jie-china-go\" target=\"_blank\" rel=\"noopener\">Google’s AlphaGo\u003c/a>, are examples of narrow AI. They can be trained to perform complex tasks such as identifying cancerous skin patterns or playing the ancient Chinese strategy game of Go. They are very far, however, from being capable to do everyday general intelligence tasks such as gardening, arguing or inventing a children’s story.\u003c/p>\n\u003cp>The cautionary prophecies of visionaries like \u003ca href=\"https://en.wikipedia.org/wiki/Open_Letter_on_Artificial_Intelligence\" target=\"_blank\" rel=\"noopener\">Elon Musk, Bill Gates and Stephen Hawking\u003c/a> against AI really are meant as an early warning against the dangers of AGI, but that is not something our children will be confronted with. Their immediate partners will be of the narrow AI kind. The future of labor depends on how well we equip them to use computers as cognitive partners .\u003c/p>\n\u003cp>\u003cem>Hype cycle for emerging technologies\u003c/em>\u003c/p>\n\u003cp>\u003cimg class=\"alignnone\" src=\"https://images.theconversation.com/files/206206/original/file-20180213-44627-13cbbah.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip\" alt=\"\" width=\"754\" height=\"480\">\u003c/p>\n\u003ch2>Better together\u003c/h2>\n\u003cp>Garry Kasparov – the chess grandmaster who was \u003ca href=\"http://www-03.ibm.com/ibm/history/ibm100/us/en/icons/deepblue/\" target=\"_blank\" rel=\"noopener\">defeated by IBM’s Deep Blue computer in 1997\u003c/a> – calls this human-machine cooperation “augmented intelligence.” He compares this “augmentation” to the mythic image of a centaur: combine a quadruped’s horsepower with the intuition of a human mind. To illustrate the potential of centaurs, he describes a freestyle chess tournament in 2005 in which any combination of human-machine teams was possible. \u003ca href=\"http://www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/\">In his words\u003c/a>:\u003c/p>\n\u003cblockquote>\u003cp>“The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and ‘coaching’ their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grand-master opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process. Human strategic guidance combined with the tactical acuity of a computer was overwhelming.”\u003c/p>\u003c/blockquote>\n\u003cp>Human-machine cognitive partnerships can amplify what each partner does best: humans are great at making intuitive and creative decisions based on \u003cem>knowledge\u003c/em> while computers are good at sifting through large amounts of data to produce information that will feed into human knowledge and decision making. We use this combination of narrow AI and human unique cognitive and motor skills every day, often without realising it. A few examples:\u003c/p>\n\u003cul>\n\u003cli>Using Internet search engines to find content (videos, images, articles) that will be helpful in preparing for a school assignment. Then combining them in creative ways in a multimedia slide presentation.\u003c/li>\n\u003cli>Using a translation algorithm to produce a first draft of a document in a different language, then manually improving the style and grammar of the final document.\u003c/li>\n\u003cli>Driving a car to an unknown destination using a smartphone GPS application to navigate through alternative routes based on real-time traffic information;\u003c/li>\n\u003cli>Relying on a movie-streaming platform to shortlist films you are going to appreciate based on your recent history; making the final choice based on mood, social context, serendipity.\u003c/li>\n\u003c/ul>\n\u003cp>Netflix is a \u003ca href=\"https://blog.kissmetrics.com/how-netflix-uses-analytics/\" target=\"_blank\" rel=\"noopener\">great example\u003c/a> of this collaboration at its best. By using machine-learning algorithms to analyze how often and how long people watch their content, they can determine how engaging each story component is to certain audiences. This information is used by screenwriters, producers and directors to better understand what and how to create new content. Virtual-reality technology allows content creators to experiment with different storytelling perspectives before they ever shoot a single scene.\u003c/p>\n\u003cp>Likewise, architects can rely on computers to adjust the functional aspects of their work. Software engineers can focus on the overall systems structure while machines provide ready-to-use code snippets and libraries to speed up the process. Marketers rely on big data and visualisation tools to determine how to better understand customer needs and develop better products and services. None of these tasks could be accomplished by AI without human guidance. Conversely, human creativity and productivity have been enormously leveraged by this AI support, allowing to achieve better quality solutions at lower costs.\u003c/p>\n\u003ch2>Losses and gains\u003c/h2>\n\u003cp>As innovation accelerates, thousands of jobs \u003cem>will\u003c/em> disappear, just as it has happened in the previous cycles of industrial revolutions. Machines powered by narrow AI algorithms can already perform certain 3-D tasks (“dull, dirty and dangerous”) much better than humans. This may create enormous pain for those who are losing their jobs over the next few years, particularly if they don’t acquire the computer-related skills that would enable them to find more creative opportunities. We must learn from the previous waves of creative destruction if we are to mitigate human suffering and increasing inequality.\u003c/p>\n\u003cp>For example, some statistics indicate that as much as \u003ca href=\"https://www.cnbc.com/2016/09/02/driverless-cars-will-kill-the-most-jobs-in-select-us-states.html\">3 percent of the population\u003c/a> in developed countries work as drivers. When automated cars become a reality in the next 15 to 25 years, we must offer people who will be \u003ca href=\"https://en.wikipedia.org/wiki/Structural_unemployment\">“structurally unemployed”\u003c/a> some sort of compensation income, training and repositioning opportunities.\u003c/p>\n\u003cp>Fortunately, the \u003ca href=\"http://www.nytimes.com/2000/06/10/your-money/half-a-century-later-economists-creative-destruction-theory-is.html\" target=\"_blank\" rel=\"noopener\">Schumpeterian waves of destructive innovation\u003c/a> also create jobs. History has shown that disruptive innovations are not always a zero-sum game. On the long run, the loss of low-added-value jobs to machines can have a positive impact in the overall quality of life of most workers. The \u003ca href=\"http://www.aei.org/publication/what-atms-bank-tellers-rise-robots-and-jobs/\" target=\"_blank\" rel=\"noopener\">ATM paradox\u003c/a> is a good example of this. As the use of automatic teller machines spread in the 1980s and ‘90s, many predicted massive unemployment in the banking sector. Instead, ATMs created more jobs as the cost of opening new agencies decreased. The number of agencies multiplied, as did the portfolio of banking products. Thanks to automation, going to the bank offers a much better customer experience than in previous decades. And the jobs in the industry became better paid and were of better quality.\u003c/p>\n\u003cp>A similar phenomenon happened with the textile industry in the 19th century. Better human-machine coordination leveraged productivity and created customer value, increasing the overall market size and creating new employment opportunities. Likewise, we may predict that as low-quality jobs continue to disappear, AI-assisted jobs will emerge to fulfill the increasing demand for more productive, ecological and creative products. More productivity may mean shorter work weeks and more time for family and entertainment, which may lead to more sustainable forms of value creation and, ultimately, more jobs.\u003c/p>\n\u003ch2>Adapting to the future\u003c/h2>\n\u003cp>This optimist scenario assumes, however, that education systems will do a better job of preparing our children to become good at what humans do best: creative and critical thinking. Less learning-by-heart (after all, most information is one Google search away) and more learning-by-doing. Fewer clerical skills and more philosophical insights about human nature and how to cater to its infinite needs for art and culture. As Apple founder and CEO Steve Jobs \u003ca href=\"https://thenextweb.com/apple/2011/09/20/the-top-20-most-inspiring-steve-jobs-quotes/\">famously said\u003c/a>:\u003c/p>\n\u003cblockquote>\u003cp>“What made the Macintosh great was that the people working on it were musicians and poets and artists and zoologists and historians who also happened to be the best computer scientists in the world.”\u003c/p>\u003c/blockquote>\n\u003cp>To become creative and critical thinkers, our children will need knowledge and wisdom more than raw data points. They need to ask “why?”, “how?” and “what if?” more often than “what?”, who?“ and \"when?” And they must construct this knowledge by relying on databases as cognitive partners as soon as they learn how to read and write. Constructivist methods such as the \u003ca href=\"https://theconversation.com/explainer-what-is-the-hybrid-classroom-and-is-it-the-future-of-education-37611\" target=\"_blank\" rel=\"noopener\">“flipped classroom”\u003c/a> approach are a good step in that direction. In flipped classrooms, students are told to search for specific contents on the web at home and to come to class ready to apply what they learned in a collaborative project supervised by the teacher. Thus they do their “homework” (exercise) in class and they have web “lectures” at home, optimising class time to do what computers cannot help them to do: create, develop and apply complex ideas collaboratively with their peers.\u003c/p>\n\u003cp>\u003cimg src=\"https://counter.theconversation.com/content/91672/count.gif?distributor=republish-lightbox-basic\" alt=\"The Conversation\" width=\"1\" height=\"1\">Thus, the future of human-machine collaboration looks less like the scenario in the \u003cem>Terminator\u003c/em> movies and more like a \u003ca href=\"https://www.youtube.com/watch?v=lG7DGMgfOb8\" target=\"_blank\" rel=\"noopener\">\u003cem>Minority Report\u003c/em>\u003c/a>-style of “augmented intelligence”. There will be jobs if we adapt the education system to equip our children to do what humans are good at: to think critically and creatively, to develop knowledge and wisdom, to appreciate and create beautiful works of art. That does not mean it will be a painless transition. Machines and automation will likely take away millions of low-quality jobs as it has happened in the past. But better-quality jobs will likely replace them, requiring less physical effort and shorter hours to deliver better results. At least until artificial general intelligence becomes a reality – then all bets are off. But this will likely be our great-grandchildren’s problem.\u003c/p>\n\u003cp>\u003cem>\u003ca href=\"https://theconversation.com/profiles/marcos-lima-221766\">Marcos Lima\u003c/a>, Responsable de la filière Marketing Innovation and Distribution, EMLV (Ecole de Management Léonard de Vinci), \u003ca href=\"http://theconversation.com/institutions/pole-leonard-de-vinci-ugei-2391\">Pôle Léonard de Vinci – UGEI\u003c/a>\u003c/em>\u003c/p>\n\u003cp>[ad floatright]\u003c/p>\n\u003cp>\u003cem>This article was originally published on \u003ca href=\"http://theconversation.com\">The Conversation\u003c/a>. Read the \u003ca href=\"https://theconversation.com/no-artificial-intelligence-wont-steal-your-childrens-jobs-it-will-make-them-more-creative-and-productive-91672\">original article\u003c/a>.\u003c/em>\u003c/p>\n\n","blocks":[],"excerpt":"The future of labor depends on how well we equip the next generation to use computers as cognitive partners.\r\n","status":"publish","parent":0,"modified":1535389313,"stats":{"hasAudio":false,"hasVideo":false,"hasChartOrMap":false,"iframeSrcs":[],"hasGoogleForm":false,"hasGallery":false,"hasHearkenModule":false,"hasPolis":false,"paragraphCount":27,"wordCount":1965},"headData":{"title":"There Will Be Jobs for Your Kids, Despite Artificial Intelligence (Commentary) | KQED","description":"The future of labor depends on how well we equip the next generation to use computers as cognitive partners.\r\n","ogTitle":"","ogDescription":"","ogImgId":"","twTitle":"","twDescription":"","twImgId":"","schema":{"@context":"http://schema.org","@type":"Article","headline":"There Will Be Jobs for Your Kids, Despite Artificial Intelligence (Commentary)","datePublished":"2018-02-15T22:33:49.000Z","dateModified":"2018-08-27T17:01:53.000Z","image":"https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"}},"disqusIdentifier":"439513 https://ww2.kqed.org/futureofyou/?p=439513","disqusUrl":"https://ww2.kqed.org/futureofyou/2018/02/15/your-kids-will-still-have-jobs-despite-artificial-intelligence-commentary/","disqusTitle":"There Will Be Jobs for Your Kids, Despite Artificial Intelligence (Commentary)","source":"KQED Future of You","nprByline":"Marcos Lima\u003cbr />The Conversation","path":"/futureofyou/439513/your-kids-will-still-have-jobs-despite-artificial-intelligence-commentary","audioTrackLength":null,"parsedContent":[{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003cp>\"Whatever your job is the chances are that one of these machines can do it faster or better than you can.\"\u003c/p>\n\u003cp>No, this is not a 2018 headline about self-driving cars or one of IBM’s new supercomputers. Instead, it was published by the \u003ca href=\"https://issuu.com/bloomsburypublishing/docs/electronic_dreams_extract\" target=\"_blank\" rel=\"noopener\">\u003cem>Daily Mirror\u003c/em> in 1955\u003c/a>, when a computer took as much space as a large kitchen and had less power than a pocket calculator. They were called “electronic brains” back then, and evoked both hope and fear. And more than 20 years later, little had changed: In a \u003ca href=\"http://podplayer.net/#/?id=9183280\" target=\"_blank\" rel=\"noopener\">1978 BBC documentary\u003c/a> about silicon chips, one commentator argued that “They are the reason why Japan is abandoning its shipbuilding and why our children will grow up without jobs to go to.\"\u003c/p>\n\u003ch2>Artificial intelligence hype is not new\u003c/h2>\n\u003cp>If one types “artificial intelligence” (AI) on Google Books’ Ngram Viewer – a tool that allows us to check how often a term was printed in a book between 1800 and 2008 – we can \u003ca href=\"https://books.google.com/ngrams/graph?content=artificial+intelligence\" target=\"_blank\" rel=\"noopener\">clearly see\u003c/a> that our modern-day hype, optimism and deep concern about AI are by no means a novelty.\u003c/p>\n\u003cp>\u003cimg class=\"aligncenter\" src=\"https://images.theconversation.com/files/206209/original/file-20180213-44630-8tobd.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip\" alt=\"\" width=\"754\" height=\"333\">\u003c/p>\n\u003cp>The history of AI is a long series of booms and busts. The first “AI spring” took place between 1956 and 1974, with pioneers such as the young \u003ca href=\"https://web.media.mit.edu/%7Eminsky/\" target=\"_blank\" rel=\"noopener\">Marvin Minsky\u003c/a>. This was followed by the “first AI winter” (1974-1980), when disillusion with the gap between \u003ca href=\"https://theconversation.com/what-is-machine-learning-76759\" target=\"_blank\" rel=\"noopener\">machine learning\u003c/a> and human cognitive capacities first led to disinvestment and disinterest in the topic. A second boom (1980-1987) was followed by another “winter” (1987-2001). Since the 2000s we’ve been surfing the third “AI spring”.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"fullwidth"},"numeric":["fullwidth"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>There’s plenty of reasons to believe this latest wave of interest for AI is going to be more durable. \u003ca href=\"https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/\" target=\"_blank\" rel=\"noopener\">According to Gartner Research\u003c/a>, technologies typically go from a “peak of inflated expectations” through a “trough of disillusionment” until they finally reach a “plateau of productivity”. AI-intensive technologies such as virtual assistants, the Internet of Things, smart robots and augmented data discovery are about to reach the peak. \u003ca href=\"https://theconversation.com/deep-learning-and-neural-networks-77259\" target=\"_blank\" rel=\"noopener\">Deep learning\u003c/a>, machine learning and cognitive expert advisors are expected to reach the plateau of mainstream applications in two to five years.\u003c/p>\n\u003ch2>Narrow intelligence\u003c/h2>\n\u003cp>We finally seem to have enough computing power to credibly develop what is called “narrow AI”, of which all the aforementioned technologies are an example. These are not to be confused with “artificial general intelligence” (AGI), which scientist and futurologist Ray Kurzweil called \u003ca href=\"http://www.kurzweilai.net/are-we-spiritual-machines-ray-kurzweil-critics-strong-ai\" target=\"_blank\" rel=\"noopener\">“strong AI.”\u003c/a> Some of the most advanced AI systems to date, such as IBM’s Watson supercomputer or \u003ca href=\"https://www.theguardian.com/technology/2017/may/23/alphago-google-ai-beats-ke-jie-china-go\" target=\"_blank\" rel=\"noopener\">Google’s AlphaGo\u003c/a>, are examples of narrow AI. They can be trained to perform complex tasks such as identifying cancerous skin patterns or playing the ancient Chinese strategy game of Go. They are very far, however, from being capable to do everyday general intelligence tasks such as gardening, arguing or inventing a children’s story.\u003c/p>\n\u003cp>The cautionary prophecies of visionaries like \u003ca href=\"https://en.wikipedia.org/wiki/Open_Letter_on_Artificial_Intelligence\" target=\"_blank\" rel=\"noopener\">Elon Musk, Bill Gates and Stephen Hawking\u003c/a> against AI really are meant as an early warning against the dangers of AGI, but that is not something our children will be confronted with. Their immediate partners will be of the narrow AI kind. The future of labor depends on how well we equip them to use computers as cognitive partners .\u003c/p>\n\u003cp>\u003cem>Hype cycle for emerging technologies\u003c/em>\u003c/p>\n\u003cp>\u003cimg class=\"alignnone\" src=\"https://images.theconversation.com/files/206206/original/file-20180213-44627-13cbbah.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip\" alt=\"\" width=\"754\" height=\"480\">\u003c/p>\n\u003ch2>Better together\u003c/h2>\n\u003cp>Garry Kasparov – the chess grandmaster who was \u003ca href=\"http://www-03.ibm.com/ibm/history/ibm100/us/en/icons/deepblue/\" target=\"_blank\" rel=\"noopener\">defeated by IBM’s Deep Blue computer in 1997\u003c/a> – calls this human-machine cooperation “augmented intelligence.” He compares this “augmentation” to the mythic image of a centaur: combine a quadruped’s horsepower with the intuition of a human mind. To illustrate the potential of centaurs, he describes a freestyle chess tournament in 2005 in which any combination of human-machine teams was possible. \u003ca href=\"http://www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/\">In his words\u003c/a>:\u003c/p>\n\u003cblockquote>\u003cp>“The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and ‘coaching’ their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grand-master opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process. Human strategic guidance combined with the tactical acuity of a computer was overwhelming.”\u003c/p>\u003c/blockquote>\n\u003cp>Human-machine cognitive partnerships can amplify what each partner does best: humans are great at making intuitive and creative decisions based on \u003cem>knowledge\u003c/em> while computers are good at sifting through large amounts of data to produce information that will feed into human knowledge and decision making. We use this combination of narrow AI and human unique cognitive and motor skills every day, often without realising it. A few examples:\u003c/p>\n\u003cul>\n\u003cli>Using Internet search engines to find content (videos, images, articles) that will be helpful in preparing for a school assignment. Then combining them in creative ways in a multimedia slide presentation.\u003c/li>\n\u003cli>Using a translation algorithm to produce a first draft of a document in a different language, then manually improving the style and grammar of the final document.\u003c/li>\n\u003cli>Driving a car to an unknown destination using a smartphone GPS application to navigate through alternative routes based on real-time traffic information;\u003c/li>\n\u003cli>Relying on a movie-streaming platform to shortlist films you are going to appreciate based on your recent history; making the final choice based on mood, social context, serendipity.\u003c/li>\n\u003c/ul>\n\u003cp>Netflix is a \u003ca href=\"https://blog.kissmetrics.com/how-netflix-uses-analytics/\" target=\"_blank\" rel=\"noopener\">great example\u003c/a> of this collaboration at its best. By using machine-learning algorithms to analyze how often and how long people watch their content, they can determine how engaging each story component is to certain audiences. This information is used by screenwriters, producers and directors to better understand what and how to create new content. Virtual-reality technology allows content creators to experiment with different storytelling perspectives before they ever shoot a single scene.\u003c/p>\n\u003cp>Likewise, architects can rely on computers to adjust the functional aspects of their work. Software engineers can focus on the overall systems structure while machines provide ready-to-use code snippets and libraries to speed up the process. Marketers rely on big data and visualisation tools to determine how to better understand customer needs and develop better products and services. None of these tasks could be accomplished by AI without human guidance. Conversely, human creativity and productivity have been enormously leveraged by this AI support, allowing to achieve better quality solutions at lower costs.\u003c/p>\n\u003ch2>Losses and gains\u003c/h2>\n\u003cp>As innovation accelerates, thousands of jobs \u003cem>will\u003c/em> disappear, just as it has happened in the previous cycles of industrial revolutions. Machines powered by narrow AI algorithms can already perform certain 3-D tasks (“dull, dirty and dangerous”) much better than humans. This may create enormous pain for those who are losing their jobs over the next few years, particularly if they don’t acquire the computer-related skills that would enable them to find more creative opportunities. We must learn from the previous waves of creative destruction if we are to mitigate human suffering and increasing inequality.\u003c/p>\n\u003cp>For example, some statistics indicate that as much as \u003ca href=\"https://www.cnbc.com/2016/09/02/driverless-cars-will-kill-the-most-jobs-in-select-us-states.html\">3 percent of the population\u003c/a> in developed countries work as drivers. When automated cars become a reality in the next 15 to 25 years, we must offer people who will be \u003ca href=\"https://en.wikipedia.org/wiki/Structural_unemployment\">“structurally unemployed”\u003c/a> some sort of compensation income, training and repositioning opportunities.\u003c/p>\n\u003cp>Fortunately, the \u003ca href=\"http://www.nytimes.com/2000/06/10/your-money/half-a-century-later-economists-creative-destruction-theory-is.html\" target=\"_blank\" rel=\"noopener\">Schumpeterian waves of destructive innovation\u003c/a> also create jobs. History has shown that disruptive innovations are not always a zero-sum game. On the long run, the loss of low-added-value jobs to machines can have a positive impact in the overall quality of life of most workers. The \u003ca href=\"http://www.aei.org/publication/what-atms-bank-tellers-rise-robots-and-jobs/\" target=\"_blank\" rel=\"noopener\">ATM paradox\u003c/a> is a good example of this. As the use of automatic teller machines spread in the 1980s and ‘90s, many predicted massive unemployment in the banking sector. Instead, ATMs created more jobs as the cost of opening new agencies decreased. The number of agencies multiplied, as did the portfolio of banking products. Thanks to automation, going to the bank offers a much better customer experience than in previous decades. And the jobs in the industry became better paid and were of better quality.\u003c/p>\n\u003cp>A similar phenomenon happened with the textile industry in the 19th century. Better human-machine coordination leveraged productivity and created customer value, increasing the overall market size and creating new employment opportunities. Likewise, we may predict that as low-quality jobs continue to disappear, AI-assisted jobs will emerge to fulfill the increasing demand for more productive, ecological and creative products. More productivity may mean shorter work weeks and more time for family and entertainment, which may lead to more sustainable forms of value creation and, ultimately, more jobs.\u003c/p>\n\u003ch2>Adapting to the future\u003c/h2>\n\u003cp>This optimist scenario assumes, however, that education systems will do a better job of preparing our children to become good at what humans do best: creative and critical thinking. Less learning-by-heart (after all, most information is one Google search away) and more learning-by-doing. Fewer clerical skills and more philosophical insights about human nature and how to cater to its infinite needs for art and culture. As Apple founder and CEO Steve Jobs \u003ca href=\"https://thenextweb.com/apple/2011/09/20/the-top-20-most-inspiring-steve-jobs-quotes/\">famously said\u003c/a>:\u003c/p>\n\u003cblockquote>\u003cp>“What made the Macintosh great was that the people working on it were musicians and poets and artists and zoologists and historians who also happened to be the best computer scientists in the world.”\u003c/p>\u003c/blockquote>\n\u003cp>To become creative and critical thinkers, our children will need knowledge and wisdom more than raw data points. They need to ask “why?”, “how?” and “what if?” more often than “what?”, who?“ and \"when?” And they must construct this knowledge by relying on databases as cognitive partners as soon as they learn how to read and write. Constructivist methods such as the \u003ca href=\"https://theconversation.com/explainer-what-is-the-hybrid-classroom-and-is-it-the-future-of-education-37611\" target=\"_blank\" rel=\"noopener\">“flipped classroom”\u003c/a> approach are a good step in that direction. In flipped classrooms, students are told to search for specific contents on the web at home and to come to class ready to apply what they learned in a collaborative project supervised by the teacher. Thus they do their “homework” (exercise) in class and they have web “lectures” at home, optimising class time to do what computers cannot help them to do: create, develop and apply complex ideas collaboratively with their peers.\u003c/p>\n\u003cp>\u003cimg src=\"https://counter.theconversation.com/content/91672/count.gif?distributor=republish-lightbox-basic\" alt=\"The Conversation\" width=\"1\" height=\"1\">Thus, the future of human-machine collaboration looks less like the scenario in the \u003cem>Terminator\u003c/em> movies and more like a \u003ca href=\"https://www.youtube.com/watch?v=lG7DGMgfOb8\" target=\"_blank\" rel=\"noopener\">\u003cem>Minority Report\u003c/em>\u003c/a>-style of “augmented intelligence”. There will be jobs if we adapt the education system to equip our children to do what humans are good at: to think critically and creatively, to develop knowledge and wisdom, to appreciate and create beautiful works of art. That does not mean it will be a painless transition. Machines and automation will likely take away millions of low-quality jobs as it has happened in the past. But better-quality jobs will likely replace them, requiring less physical effort and shorter hours to deliver better results. At least until artificial general intelligence becomes a reality – then all bets are off. But this will likely be our great-grandchildren’s problem.\u003c/p>\n\u003cp>\u003cem>\u003ca href=\"https://theconversation.com/profiles/marcos-lima-221766\">Marcos Lima\u003c/a>, Responsable de la filière Marketing Innovation and Distribution, EMLV (Ecole de Management Léonard de Vinci), \u003ca href=\"http://theconversation.com/institutions/pole-leonard-de-vinci-ugei-2391\">Pôle Léonard de Vinci – UGEI\u003c/a>\u003c/em>\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"floatright"},"numeric":["floatright"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>\u003cem>This article was originally published on \u003ca href=\"http://theconversation.com\">The Conversation\u003c/a>. Read the \u003ca href=\"https://theconversation.com/no-artificial-intelligence-wont-steal-your-childrens-jobs-it-will-make-them-more-creative-and-productive-91672\">original article\u003c/a>.\u003c/em>\u003c/p>\n\n\u003c/div>\u003c/p>","attributes":{"named":{},"numeric":[]}}],"link":"/futureofyou/439513/your-kids-will-still-have-jobs-despite-artificial-intelligence-commentary","authors":["byline_futureofyou_439513"],"categories":["futureofyou_1062","futureofyou_1","futureofyou_73","futureofyou_1061"],"tags":["futureofyou_849","futureofyou_1460"],"featImg":"futureofyou_439529","label":"source_futureofyou_439513"},"futureofyou_435986":{"type":"posts","id":"futureofyou_435986","meta":{"index":"posts_1591205157","site":"futureofyou","id":"435986","score":null,"sort":[1507824804000]},"guestAuthors":[],"slug":"capturing-the-sound-of-depression-in-the-human-voice","title":"Capturing the Sound of Depression in the Human Voice","publishDate":1507824804,"format":"standard","headTitle":"KQED Future of You | KQED Science","labelTerm":{},"content":"\u003cp>In any given year, nearly 1 in 5 adults in the U.S. suffer from a mental illness, yet fewer than half of those suffering receive treatment.\u003c/p>\n\u003caside class=\"alignright\">Signs of depression in the human voice might help to diagnose mental health problems\n\u003cul>\n\u003cli>Speaking lower, flatter and softer\u003c/li>\n\u003cli>Sounding labored, with more pauses, starts and stops\u003c/li>\n\u003cli>Sounding strained or breathy\u003c/li>\n\u003c/ul>\n\u003c/aside>\n\u003cp>In an attempt to fill that gap, companies are developing digital technology to help doctors diagnose, monitor and treat psychiatric disorders.\u003c/p>\n\u003cp>The behavioral health startup Ellipsis Health, based in San Francisco, uses machine learning to analyze audio recordings of conversations between doctors and patients during exams. The software works as a screening tool to flag patients whose speech matches the voice patterns of depressed individuals, alerting clinicians to follow up with a full diagnostic interview.\u003c/p>\n\u003cp>Meanwhile, Boston's \u003ca href=\"http://www.cogitocorp.com/\" target=\"_blank\" rel=\"noopener\">Cogito\u003c/a> has developed an app to use metadata from patients' phones to alert health care providers about sudden changes in behavior that might be linked to mental health.\u003c/p>\n\u003cp>\u003cstrong>Difficulty of Diagnosis\u003c/strong>\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>One reason for the lack of treatment for mental illness is that conditions like depression, anxiety and post‐traumatic stress disorder are difficult to diagnose.\u003c/p>\n\u003cp>There are no biological markers of mental illness that can be picked up in a blood test or a brain scan, so physicians must rely on patients’ self‐reports of their symptoms and on mental health questionnaires.\u003c/p>\n\u003cp>But self‐reporting and doctor observations can be highly subjective. Low energy, for example, can be a sign of depression, a normal response to a busy schedule, or an indicator of hypothyroidism. Subsequently, many physicians miss or misdiagnose psychiatric disorders; one \u003ca href=\"http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(09)60879-5/abstract\" target=\"_blank\" rel=\"noopener\">study \u003c/a>found that primary care doctors correctly identify depression in patients only 50 percent of the time.\u003c/p>\n\u003cp>However, there are a few consistent physiological changes that take place in the body when something is off in your brain. Some researchers think these types of changes could be used as objective flags for mental illness.\u003c/p>\n\u003cp>\u003cstrong>It’s Not What You Say, But How You Say It\u003c/strong>\u003c/p>\n\u003cp>When someone is depressed, their range of pitch and volume drop, so they tend to speak \u003ca href=\"http://ieeexplore.ieee.org/document/7117386/?reload=true\" target=\"_blank\" rel=\"noopener\">lower, flatter and softer\u003c/a>. Speech also sounds labored, with more pauses, starts and stops. Another key indicator is the tension or relaxation of the vocal cords, which can make speech sound strained or breathy. Too much tension or relaxation has been linked to depression and \u003ca href=\"http://ieeexplore.ieee.org/document/7384418/\" target=\"_blank\" rel=\"noopener\">suicide risk\u003c/a>. Depressed patients’ tongues and breath may also become uncoordinated, resulting in a slight slurring of speech.\u003c/p>\n\u003cp>These types of vocal traits — called paraverbal features — are detectable in other mental illnesses too, including bipolar and post‐traumatic stress disorder.\u003c/p>\n\u003cp>Researchers have been studying these different paraverbal patterns for over a decade, but they’ve only recently been able to make use of them with the rise of advanced computer analytics. Some of the qualities are noticeable to a trained ear, but others are more subtle.\u003c/p>\n\u003cp>Computer algorithms can pick up on differences in tone that a human might miss, and they can also quantify them. This technology helps clinicians track patients over time by comparing individuals to their own baselines, particularly important if a person has a naturally deep or breathy voice that is not indicative of depression.\u003c/p>\n\u003cp>“There’s no doubt that paraverbal features can be helpful in making clinical diagnoses,” says Danielle Ramo, an assistant professor of psychiatry at UCSF who is not affiliated with either company. “To the extent that machines are able to take advantage of paraverbal features in communication, that is a step forward in using machines to inform clinical diagnoses or treatment planning.”\u003c/p>\n\u003cp>Ellipsis used these vocal traits to create its tool. The program was initially trained by taking millions of conversations between nondepressed individuals and mining them for key features in speech patterns, such as pitch, cadence and enunciation. Data scientists then added conversations, data from mental health questionnaires and clinical information, all from depressed patients. That trained the software to identify vocal features indicative of depression.\u003c/p>\n\u003cp>One potential benefit of the Ellipsis program is that it could alert physicians if their patients may be depressed, even if that’s not the reason they went to the doctor in the first place.\u003c/p>\n\u003cp>“There are a lot of problems in the process of screening for mental health disorders,\" says Mark Richman, medical director of the Disease Management Program at Northwell Health, a health network in Long Island and New York City that is in talks with Ellipsis to use its program. \"Some of that has to do with limited sensitivity of the tools that are used to do that, some of it has to do with limited time to address and identify mental health disorders.”\u003c/p>\n\u003cp>Richman says he hopes a tool like the one from Ellipsis will help doctors catch patients who might otherwise fall through the cracks, but without taking up valuable face time in the exam room.\u003c/p>\n\u003cp>Ellipsis founder and CEO Mainul Mondal says that making doctor appointments more efficient was a key focus for the company.\u003c/p>\n\u003cp>“People like talking to their health teams. They feel loyalty\u003cbr>\nand the trust is there, and they’re already having the conversations,” he says.\u003c/p>\n\u003cp>By capturing and utilizing the conversation — with the patient’s consent — clinicians could screen every patient for depression without taking up extra time, following up with only those who are flagged as high risk.\u003c/p>\n\u003cp>Integrating the software into the normal flow of a doctor’s visit is the next step, says Elizabeth Shriberg, the Ellipsis chief scientific officer. Including Northwell, Ellipsis is now in talks with other large health care providers around the country to introduce the program into exam rooms, the company says.\u003c/p>\n\u003cp>\u003cstrong>Monitoring From Afar\u003c/strong>\u003c/p>\n\u003cp>Other physicians want to use voice and behavior analysis technology to learn more about what happens to patients when they’re \u003cem>outside\u003c/em> the exam room.\u003c/p>\n\u003cfigure id=\"attachment_436000\" class=\"wp-caption alignright\" style=\"max-width: 248px\">\u003ca href=\"https://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2017/10/companion-2-0-android-Home-1.png\">\u003cimg class=\"wp-image-436000 \" src=\"https://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2017/10/companion-2-0-android-Home-1-800x1422.png\" alt=\"\" width=\"248\" height=\"441\" srcset=\"https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-800x1422.png 800w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-160x284.png 160w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-768x1365.png 768w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-1020x1813.png 1020w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-1180x2098.png 1180w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-960x1707.png 960w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-240x427.png 240w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-375x667.png 375w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-520x924.png 520w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1.png 1440w\" sizes=\"(max-width: 248px) 100vw, 248px\">\u003c/a>\u003cfigcaption class=\"wp-caption-text\">Image from the dashboard of Cogito's Companion app, meant to diagnose depression and other mental health conditions. \u003ccite>(Cogito)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cp>“We often get data only when people come into a clinic … and we know that there's a lot that goes on, obviously, outside of the clinic walls,” says David Ahern, director of the program in Behavioral Informatics and eHealth at Brigham and Women’s Hospital in Boston. “It's this huge unmet need of both understanding the nature of a … mental health disorder as it evolves over time and the experience of patients over time.”\u003c/p>\n\u003cp>Ahern leads a \u003ca href=\"https://clinicaltrials.gov/ct2/show/NCT02167373?term=cogito&rank=1\" target=\"_blank\" rel=\"noopener\">clinical trial\u003c/a> testing the efficacy of Cogito's Companion app. Cogito was founded eight years ago as a spin-off from the MIT Media Lab in Boston. In addition to Companion, it makes software that gives real‐time feedback to customer call centers.\u003c/p>\n\u003cp>With patients' consent, the Companion app mines background metadata from their phone, including text frequency, call logs and geolocation. Using this data, the program creates a daily score for each patient, which is sent to their care team, alerting them if sudden changes in behavior might be linked to a decline in mental health. For example, if a patient starts to text less and has fewer or shorter calls, it may signal that they’re isolating themselves. Their caregiver can then reach out immediately to see if they are all right rather than having to wait until the next visit.\u003c/p>\n\u003cp>Patients also record a short audio diary a few times a week, which the app analyzes for nonverbal markers of depression, such as tenseness or breathiness, low pitch, volume or range. These results are also included in the patient’s daily score, giving the care team several objective measures of their mental state. In addition to depression, the app is also being \u003ca href=\"https://clinicaltrials.gov/ct2/show/NCT02742064?term=cogito&rank=2\" target=\"_blank\" rel=\"noopener\">tested\u003c/a> on patients with bipolar disorder and post‐traumatic disorder, with the goal of predicting the onset of acute psychiatric episodes and suicide risk.\u003c/p>\n\u003cp>Skyler Place, vice president of behavioral science at Cogito, says he hopes doctors will use the system to be more proactive, not just in patients’ mental health care, but in their overall quality of life. In another trial for veterans at risk for post‐traumatic stress disorder and suicide, clinicians were able to detect major lifestyle changes through the app, as when one person lost his job and another became homeless.\u003c/p>\n\u003cp>“While the original goal was suicide prevention, in addition to being able to capture that risk, it’s really able to provide an overall risk score for the veteran population, and the clinicians are able to then provide the right service to these veterans in the moment when they need them.”\u003c/p>\n\u003cp>\u003cstrong>Replacing Clinicians? Not so Fast\u003c/strong>\u003c/p>\n\u003cp>As with the adoption of many new technologies, users may be reasonably concerned about privacy. Both Cogito and Ellipsis say that all the proper precautions are taken to store and protect the data, and in many cases the content of the conversations or voice recordings is irrelevant and even discarded.\u003c/p>\n\u003cp>The voice screening software is also not perfect; Cogito’s is currently about 75 percent accurate at flagging mental illness\u003cstrong> \u003c/strong>as compared to clinical interviews with mental health professionals. Ellipsis declined to state how accurate its software is.\u003c/p>\n\u003cp>Adam Miner, a clinical psychologist and instructor at\u003cbr>\nStanford University, says that, “Clinicians regularly take into account patient's tone and vocal patterns when making diagnostic decisions. If a new technology can help measure, or compare patterns over time, there is the potential to add value.” However, he cautions, there are “risks in oversimplifying the complexity of medical diagnoses.”\u003c/p>\n\u003cp>The two companies were quick to emphasize that the technology is not a replacement for human clinicians, but simply an aid or tool, akin to a blood test or an electrocardiogram.\u003c/p>\n\u003cp>“It's a buddy for health teams,” says Mondal. “It is an adviser for behavioral health; you flag patients so health systems can intervene.” After a patient is flagged as needing additional attention, either before or after diagnosis, it is still up to the clinician to administer care, an interaction that hasn’t been disrupted by artificial intelligence.\u003c/p>\n\u003cp>[ad floatright]\u003c/p>\n\u003cp>Yet.\u003c/p>\n\n","blocks":[],"excerpt":"One company aims to use data from patients' voices to diagnose depression, and another wants to look at patients' use of their smartphones to alert providers to possible changes in mental health.","status":"publish","parent":0,"modified":1507836868,"stats":{"hasAudio":false,"hasVideo":false,"hasChartOrMap":false,"iframeSrcs":[],"hasGoogleForm":false,"hasGallery":false,"hasHearkenModule":false,"hasPolis":false,"paragraphCount":40,"wordCount":1722},"headData":{"title":"Capturing the Sound of Depression in the Human Voice | KQED","description":"One company aims to use data from patients' voices to diagnose depression, and another wants to look at patients' use of their smartphones to alert providers to possible changes in mental health.","ogTitle":"","ogDescription":"","ogImgId":"","twTitle":"","twDescription":"","twImgId":"","schema":{"@context":"http://schema.org","@type":"Article","headline":"Capturing the Sound of Depression in the Human Voice","datePublished":"2017-10-12T16:13:24.000Z","dateModified":"2017-10-12T19:34:28.000Z","image":"https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"}},"disqusIdentifier":"435986 https://ww2.kqed.org/futureofyou/?p=435986","disqusUrl":"https://ww2.kqed.org/futureofyou/2017/10/12/capturing-the-sound-of-depression-in-the-human-voice/","disqusTitle":"Capturing the Sound of Depression in the Human Voice","source":"Future of You","nprByline":"Dana Smith\u003cbr />Future of You","path":"/futureofyou/435986/capturing-the-sound-of-depression-in-the-human-voice","audioTrackLength":null,"parsedContent":[{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003cp>In any given year, nearly 1 in 5 adults in the U.S. suffer from a mental illness, yet fewer than half of those suffering receive treatment.\u003c/p>\n\u003caside class=\"alignright\">Signs of depression in the human voice might help to diagnose mental health problems\n\u003cul>\n\u003cli>Speaking lower, flatter and softer\u003c/li>\n\u003cli>Sounding labored, with more pauses, starts and stops\u003c/li>\n\u003cli>Sounding strained or breathy\u003c/li>\n\u003c/ul>\n\u003c/aside>\n\u003cp>In an attempt to fill that gap, companies are developing digital technology to help doctors diagnose, monitor and treat psychiatric disorders.\u003c/p>\n\u003cp>The behavioral health startup Ellipsis Health, based in San Francisco, uses machine learning to analyze audio recordings of conversations between doctors and patients during exams. The software works as a screening tool to flag patients whose speech matches the voice patterns of depressed individuals, alerting clinicians to follow up with a full diagnostic interview.\u003c/p>\n\u003cp>Meanwhile, Boston's \u003ca href=\"http://www.cogitocorp.com/\" target=\"_blank\" rel=\"noopener\">Cogito\u003c/a> has developed an app to use metadata from patients' phones to alert health care providers about sudden changes in behavior that might be linked to mental health.\u003c/p>\n\u003cp>\u003cstrong>Difficulty of Diagnosis\u003c/strong>\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"fullwidth"},"numeric":["fullwidth"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>One reason for the lack of treatment for mental illness is that conditions like depression, anxiety and post‐traumatic stress disorder are difficult to diagnose.\u003c/p>\n\u003cp>There are no biological markers of mental illness that can be picked up in a blood test or a brain scan, so physicians must rely on patients’ self‐reports of their symptoms and on mental health questionnaires.\u003c/p>\n\u003cp>But self‐reporting and doctor observations can be highly subjective. Low energy, for example, can be a sign of depression, a normal response to a busy schedule, or an indicator of hypothyroidism. Subsequently, many physicians miss or misdiagnose psychiatric disorders; one \u003ca href=\"http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(09)60879-5/abstract\" target=\"_blank\" rel=\"noopener\">study \u003c/a>found that primary care doctors correctly identify depression in patients only 50 percent of the time.\u003c/p>\n\u003cp>However, there are a few consistent physiological changes that take place in the body when something is off in your brain. Some researchers think these types of changes could be used as objective flags for mental illness.\u003c/p>\n\u003cp>\u003cstrong>It’s Not What You Say, But How You Say It\u003c/strong>\u003c/p>\n\u003cp>When someone is depressed, their range of pitch and volume drop, so they tend to speak \u003ca href=\"http://ieeexplore.ieee.org/document/7117386/?reload=true\" target=\"_blank\" rel=\"noopener\">lower, flatter and softer\u003c/a>. Speech also sounds labored, with more pauses, starts and stops. Another key indicator is the tension or relaxation of the vocal cords, which can make speech sound strained or breathy. Too much tension or relaxation has been linked to depression and \u003ca href=\"http://ieeexplore.ieee.org/document/7384418/\" target=\"_blank\" rel=\"noopener\">suicide risk\u003c/a>. Depressed patients’ tongues and breath may also become uncoordinated, resulting in a slight slurring of speech.\u003c/p>\n\u003cp>These types of vocal traits — called paraverbal features — are detectable in other mental illnesses too, including bipolar and post‐traumatic stress disorder.\u003c/p>\n\u003cp>Researchers have been studying these different paraverbal patterns for over a decade, but they’ve only recently been able to make use of them with the rise of advanced computer analytics. Some of the qualities are noticeable to a trained ear, but others are more subtle.\u003c/p>\n\u003cp>Computer algorithms can pick up on differences in tone that a human might miss, and they can also quantify them. This technology helps clinicians track patients over time by comparing individuals to their own baselines, particularly important if a person has a naturally deep or breathy voice that is not indicative of depression.\u003c/p>\n\u003cp>“There’s no doubt that paraverbal features can be helpful in making clinical diagnoses,” says Danielle Ramo, an assistant professor of psychiatry at UCSF who is not affiliated with either company. “To the extent that machines are able to take advantage of paraverbal features in communication, that is a step forward in using machines to inform clinical diagnoses or treatment planning.”\u003c/p>\n\u003cp>Ellipsis used these vocal traits to create its tool. The program was initially trained by taking millions of conversations between nondepressed individuals and mining them for key features in speech patterns, such as pitch, cadence and enunciation. Data scientists then added conversations, data from mental health questionnaires and clinical information, all from depressed patients. That trained the software to identify vocal features indicative of depression.\u003c/p>\n\u003cp>One potential benefit of the Ellipsis program is that it could alert physicians if their patients may be depressed, even if that’s not the reason they went to the doctor in the first place.\u003c/p>\n\u003cp>“There are a lot of problems in the process of screening for mental health disorders,\" says Mark Richman, medical director of the Disease Management Program at Northwell Health, a health network in Long Island and New York City that is in talks with Ellipsis to use its program. \"Some of that has to do with limited sensitivity of the tools that are used to do that, some of it has to do with limited time to address and identify mental health disorders.”\u003c/p>\n\u003cp>Richman says he hopes a tool like the one from Ellipsis will help doctors catch patients who might otherwise fall through the cracks, but without taking up valuable face time in the exam room.\u003c/p>\n\u003cp>Ellipsis founder and CEO Mainul Mondal says that making doctor appointments more efficient was a key focus for the company.\u003c/p>\n\u003cp>“People like talking to their health teams. They feel loyalty\u003cbr>\nand the trust is there, and they’re already having the conversations,” he says.\u003c/p>\n\u003cp>By capturing and utilizing the conversation — with the patient’s consent — clinicians could screen every patient for depression without taking up extra time, following up with only those who are flagged as high risk.\u003c/p>\n\u003cp>Integrating the software into the normal flow of a doctor’s visit is the next step, says Elizabeth Shriberg, the Ellipsis chief scientific officer. Including Northwell, Ellipsis is now in talks with other large health care providers around the country to introduce the program into exam rooms, the company says.\u003c/p>\n\u003cp>\u003cstrong>Monitoring From Afar\u003c/strong>\u003c/p>\n\u003cp>Other physicians want to use voice and behavior analysis technology to learn more about what happens to patients when they’re \u003cem>outside\u003c/em> the exam room.\u003c/p>\n\u003cfigure id=\"attachment_436000\" class=\"wp-caption alignright\" style=\"max-width: 248px\">\u003ca href=\"https://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2017/10/companion-2-0-android-Home-1.png\">\u003cimg class=\"wp-image-436000 \" src=\"https://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2017/10/companion-2-0-android-Home-1-800x1422.png\" alt=\"\" width=\"248\" height=\"441\" srcset=\"https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-800x1422.png 800w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-160x284.png 160w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-768x1365.png 768w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-1020x1813.png 1020w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-1180x2098.png 1180w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-960x1707.png 960w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-240x427.png 240w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-375x667.png 375w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1-520x924.png 520w, https://ww2.kqed.org/app/uploads/sites/13/2017/10/companion-2-0-android-Home-1.png 1440w\" sizes=\"(max-width: 248px) 100vw, 248px\">\u003c/a>\u003cfigcaption class=\"wp-caption-text\">Image from the dashboard of Cogito's Companion app, meant to diagnose depression and other mental health conditions. \u003ccite>(Cogito)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cp>“We often get data only when people come into a clinic … and we know that there's a lot that goes on, obviously, outside of the clinic walls,” says David Ahern, director of the program in Behavioral Informatics and eHealth at Brigham and Women’s Hospital in Boston. “It's this huge unmet need of both understanding the nature of a … mental health disorder as it evolves over time and the experience of patients over time.”\u003c/p>\n\u003cp>Ahern leads a \u003ca href=\"https://clinicaltrials.gov/ct2/show/NCT02167373?term=cogito&rank=1\" target=\"_blank\" rel=\"noopener\">clinical trial\u003c/a> testing the efficacy of Cogito's Companion app. Cogito was founded eight years ago as a spin-off from the MIT Media Lab in Boston. In addition to Companion, it makes software that gives real‐time feedback to customer call centers.\u003c/p>\n\u003cp>With patients' consent, the Companion app mines background metadata from their phone, including text frequency, call logs and geolocation. Using this data, the program creates a daily score for each patient, which is sent to their care team, alerting them if sudden changes in behavior might be linked to a decline in mental health. For example, if a patient starts to text less and has fewer or shorter calls, it may signal that they’re isolating themselves. Their caregiver can then reach out immediately to see if they are all right rather than having to wait until the next visit.\u003c/p>\n\u003cp>Patients also record a short audio diary a few times a week, which the app analyzes for nonverbal markers of depression, such as tenseness or breathiness, low pitch, volume or range. These results are also included in the patient’s daily score, giving the care team several objective measures of their mental state. In addition to depression, the app is also being \u003ca href=\"https://clinicaltrials.gov/ct2/show/NCT02742064?term=cogito&rank=2\" target=\"_blank\" rel=\"noopener\">tested\u003c/a> on patients with bipolar disorder and post‐traumatic disorder, with the goal of predicting the onset of acute psychiatric episodes and suicide risk.\u003c/p>\n\u003cp>Skyler Place, vice president of behavioral science at Cogito, says he hopes doctors will use the system to be more proactive, not just in patients’ mental health care, but in their overall quality of life. In another trial for veterans at risk for post‐traumatic stress disorder and suicide, clinicians were able to detect major lifestyle changes through the app, as when one person lost his job and another became homeless.\u003c/p>\n\u003cp>“While the original goal was suicide prevention, in addition to being able to capture that risk, it’s really able to provide an overall risk score for the veteran population, and the clinicians are able to then provide the right service to these veterans in the moment when they need them.”\u003c/p>\n\u003cp>\u003cstrong>Replacing Clinicians? Not so Fast\u003c/strong>\u003c/p>\n\u003cp>As with the adoption of many new technologies, users may be reasonably concerned about privacy. Both Cogito and Ellipsis say that all the proper precautions are taken to store and protect the data, and in many cases the content of the conversations or voice recordings is irrelevant and even discarded.\u003c/p>\n\u003cp>The voice screening software is also not perfect; Cogito’s is currently about 75 percent accurate at flagging mental illness\u003cstrong> \u003c/strong>as compared to clinical interviews with mental health professionals. Ellipsis declined to state how accurate its software is.\u003c/p>\n\u003cp>Adam Miner, a clinical psychologist and instructor at\u003cbr>\nStanford University, says that, “Clinicians regularly take into account patient's tone and vocal patterns when making diagnostic decisions. If a new technology can help measure, or compare patterns over time, there is the potential to add value.” However, he cautions, there are “risks in oversimplifying the complexity of medical diagnoses.”\u003c/p>\n\u003cp>The two companies were quick to emphasize that the technology is not a replacement for human clinicians, but simply an aid or tool, akin to a blood test or an electrocardiogram.\u003c/p>\n\u003cp>“It's a buddy for health teams,” says Mondal. “It is an adviser for behavioral health; you flag patients so health systems can intervene.” After a patient is flagged as needing additional attention, either before or after diagnosis, it is still up to the clinician to administer care, an interaction that hasn’t been disrupted by artificial intelligence.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"floatright"},"numeric":["floatright"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>Yet.\u003c/p>\n\n\u003c/div>\u003c/p>","attributes":{"named":{},"numeric":[]}}],"link":"/futureofyou/435986/capturing-the-sound-of-depression-in-the-human-voice","authors":["byline_futureofyou_435986"],"categories":["futureofyou_452","futureofyou_1062","futureofyou_1","futureofyou_73"],"tags":["futureofyou_849","futureofyou_592","futureofyou_734","futureofyou_1275","futureofyou_1375"],"featImg":"futureofyou_436004","label":"source_futureofyou_435986"},"futureofyou_435378":{"type":"posts","id":"futureofyou_435378","meta":{"index":"posts_1591205157","site":"futureofyou","id":"435378","score":null,"sort":[1505326638000]},"guestAuthors":[],"slug":"can-facial-recognition-detect-sexual-orientation-controversial-stanford-study-now-under-ethical-review","title":"Study Claiming AI Can Detect Sexual Orientation Cleared for Publication","publishDate":1505326638,"format":"standard","headTitle":"KQED Future of You | KQED Science","labelTerm":{},"content":"\u003cp>\u003cstrong>Update, Sept. 19:\u003c/strong> The American Psychological Association says that a controversial research paper that applied computer facial recognition to successfully guess people’s sexual orientation has passed a review of documentation submitted by the researchers. The paper is set to be published in the association’s peer-reviewed \u003cem>Journal of Personality and Social Psychology\u003c/em>.\u003c/p>\n\u003cp>A spokesperson for the association said it completed the review last week. The association undertook the review to substantiate an institutional review board's vetting of the study, which ensured that the study met ethical guidelines.\u003c/p>\n\u003cp>\"Given the sensitive nature of photo-images used in the current study, we are currently taking this additional step with this as yet unpublished manuscript,\" a spokesperson for the APA wrote to KQED last week, before the review was completed.\u003c/p>\n\u003cp>The research, by Michal Kosinski and Yilun Wang of Stanford University, claims that a computer algorithm bested humans in distinguishing between a gay person and a straight person when analyzing images from public profiles on a dating website. (Here is a \u003ca href=\"https://osf.io/zn79k/\" target=\"_blank\" rel=\"noopener noreferrer\">preprint\u003c/a> of the study; it's not necessarily the final version of the paper.)Researchers claim AI can be taught to predict sexual orientation from analyzing photographs. LGBTQ advocates are outraged.\u003c/p>\n\u003cp>The research had led to a firestorm of criticism from LGBTQ advocates and academics since it was \u003ca href=\"https://www.economist.com/news/science-and-technology/21728614-machines-read-faces-are-coming-advances-ai-are-used-spot-signs?fsrc=scn/tw/te/bl/ed/advancesinaiareusedtospotsignsofsexuality\" target=\"_blank\" rel=\"noopener noreferrer\">first reported\u003c/a> Sept. 9 by The Economist. Two gay rights groups, Human Rights Campaign and GLAAD, \u003ca href=\"https://www.glaad.org/blog/glaad-and-hrc-call-stanford-university-responsible-media-debunk-dangerous-flawed-report\" target=\"_blank\" rel=\"noopener noreferrer\">called the research,\u003c/a> in a joint press release, \"junk science.\"\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>\u003cstrong>Analyzing Faces\u003c/strong>\u003c/p>\n\u003cp>Using basic facial-recognition technology, the researchers weeded through 130,741 public photos of men and women posted on a U.S. dating website, selecting for images that showed a single face large and clear enough to analyze. This left a pool of 35,326 pictures of 14,776 individuals. Gay and straight people, male and female, were represented evenly.\u003c/p>\n\u003cp>Software called VGG-Face analyzed the faces and looked for correlations between a person's face (nose length, jaw width, etc.) and their self-declared sexual identity on the website. Using a resulting model made up of these distinguishing characteristics, the program, when shown one photo of a gay man and one of a straight man, was able to identify their sexual orientation 81 percent of the time. For women, the success rate was 71 percent. (Accuracy increased when the model was shown more than one image of a person.) Human guessers correctly identified straight faces and gay faces just 61 percent of the time for men and 54 percent for women.\u003c/p>\n\u003cp>The researchers say in the paper that these results \"provide strong support\" for the prenatal hormone theory of gay and lesbian sexual orientation. The theory holds that under or overexposure to prenatal androgens are a key determinant of sexual orientation.\u003c/p>\n\u003cp>In an \u003ca href=\"https://docs.google.com/document/d/11oGZ1Ke3wK9E3BtOFfGfUQuuaSMR8AO2WfWH3aVke6U/preview#\" target=\"_blank\" rel=\"noopener noreferrer\">authors' note\u003c/a> (last updated Sept. 13), the researchers discuss the study's limitations at some length, including the narrow demographic characteristics of the individuals analyzed -- white people who self-reported to be gay or straight. They also expressed concerns about the implications of the study:\u003c/p>\n\u003cblockquote>\u003cp>\u003cspan style=\"font-weight: 400\">We were really disturbed by these results and spent much time considering whether they should be made public at all. We did not want to enable the very risks that we are warning against. \u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400\">Recent press reports,\u003c/span>\u003cspan style=\"font-weight: 400\"> however, suggest that governments and corporations are already using tools aimed at revealing intimate traits from faces. Facial images of billions of people are stockpiled in digital and traditional archives, including dating platforms, photo-sharing websites, and government databases. Profile pictures on Facebook, LinkedIn, and Google Plus are public by default. CCTV cameras and smartphones can be used to take pictures of others’ faces without their permission.\u003c/span>\u003c/p>\u003c/blockquote>\n\u003cp>Critics of the research expressed concerns that it will lead to the very invasion of privacy the authors seek to warn against. HRC/GLAAD also criticized the limited demographic pool used by the researchers, the \"superficial\" nature of the characteristics analyzed in the model, and the way media have represented the study.\u003c/p>\n\u003cp>The \u003ca href=\"https://docs.google.com/document/d/1UuEcSNFMduIaf0cOWdWbOV3NORLoKWdz3big4xuk7Z4/edit#\" target=\"_blank\" rel=\"noopener noreferrer\">authors responded\u003c/a> angrily, calling the HRC/GLAAD press release premature and misleading. \"\u003cspan style=\"font-weight: 400\">They do a great disservice to the LGBTQ community by dismissing our results outright without properly assessing the science behind it, and hurt the mission of the great organizations that they represent,\" they wrote. \u003c/span>\u003c/p>\n\u003cp>The researchers also stressed, in their authors' note, that they did not invent the tools used. Rather, they applied internet-available software to internet-available data, with the goal of demonstrating the privacy risks inherent in artificially intelligent technologies.\u003c/p>\n\u003cp>\"We studied existing technologies,\" wrote Kosinski and Wang, already widely used by companies and governments, to see whether they present a risk to the privacy of LGBTQ individuals.\"\u003c/p>\n\u003cp>They added, \"We were terrified to find that they do.\"\u003c/p>\n\u003cp>Such tools present a special threat, said the authors, to the privacy and safety of gay men and women living under repressive regimes where homosexuality is illegal.\u003c/p>\n\u003cp>But other LGBT academics and writers did not accept this line of reasoning. Oberlin sociology professor Greggor Mattson wrote a \u003ca href=\"https://greggormattson.com/2017/09/09/artificial-intelligence-discovers-gayface/\" target=\"_blank\" rel=\"noopener noreferrer\">takedown,\u003c/a> published on his website, describing the study as \"much less insightful than the researchers claim.\" The authors' discussion of their ethical concerns suffered from \"stunning tone-deafness,\" Mattson wrote.\u003c/p>\n\u003cp>At least one LGBT blogger, though, came to the researchers' defense.\u003c/p>\n\u003cp>Alex Bollinger, writing at LGBTQ Nation, wrote a \u003ca href=\"https://www.lgbtqnation.com/2017/09/hrc-glaad-release-silly-statement-gay-face-study/\" target=\"_blank\" rel=\"noopener noreferrer\">post \u003c/a>titled \"HRC and GLAAD release a silly statement about the ‘gay face’ study.\"\u003c/p>\n\u003cblockquote>\u003cp>\"We should take a stance of curiosity instead of judgment.\u003c/p>\n\u003cp>\"This is just one study that looked at one sample and said a few things. There will be more studies later on that will say other things. Let’s see how that all unfolds before deciding what the correct answer is.\"\u003c/p>\u003c/blockquote>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>This post was edited Oct. 9 to specify the nature of the American Psychological Association's review of the study.\u003c/em>\u003c/p>\n\n","blocks":[],"excerpt":"Stanford researchers claim AI can be taught to predict sexual orientation from photographs, and a firestorm ensues.","status":"publish","parent":0,"modified":1507591124,"stats":{"hasAudio":false,"hasVideo":false,"hasChartOrMap":false,"iframeSrcs":[],"hasGoogleForm":false,"hasGallery":false,"hasHearkenModule":false,"hasPolis":false,"paragraphCount":26,"wordCount":996},"headData":{"title":"Study Claiming AI Can Detect Sexual Orientation Cleared for Publication | KQED","description":"Stanford researchers claim AI can be taught to predict sexual orientation from photographs, and a firestorm ensues.","ogTitle":"","ogDescription":"","ogImgId":"","twTitle":"","twDescription":"","twImgId":"","schema":{"@context":"http://schema.org","@type":"Article","headline":"Study Claiming AI Can Detect Sexual Orientation Cleared for Publication","datePublished":"2017-09-13T18:17:18.000Z","dateModified":"2017-10-09T23:18:44.000Z","image":"https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"}},"disqusIdentifier":"435378 https://ww2.kqed.org/futureofyou/?p=435378","disqusUrl":"https://ww2.kqed.org/futureofyou/2017/09/13/can-facial-recognition-detect-sexual-orientation-controversial-stanford-study-now-under-ethical-review/","disqusTitle":"Study Claiming AI Can Detect Sexual Orientation Cleared for Publication","source":"Future of You","path":"/futureofyou/435378/can-facial-recognition-detect-sexual-orientation-controversial-stanford-study-now-under-ethical-review","audioTrackLength":null,"parsedContent":[{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003cp>\u003cstrong>Update, Sept. 19:\u003c/strong> The American Psychological Association says that a controversial research paper that applied computer facial recognition to successfully guess people’s sexual orientation has passed a review of documentation submitted by the researchers. The paper is set to be published in the association’s peer-reviewed \u003cem>Journal of Personality and Social Psychology\u003c/em>.\u003c/p>\n\u003cp>A spokesperson for the association said it completed the review last week. The association undertook the review to substantiate an institutional review board's vetting of the study, which ensured that the study met ethical guidelines.\u003c/p>\n\u003cp>\"Given the sensitive nature of photo-images used in the current study, we are currently taking this additional step with this as yet unpublished manuscript,\" a spokesperson for the APA wrote to KQED last week, before the review was completed.\u003c/p>\n\u003cp>The research, by Michal Kosinski and Yilun Wang of Stanford University, claims that a computer algorithm bested humans in distinguishing between a gay person and a straight person when analyzing images from public profiles on a dating website. (Here is a \u003ca href=\"https://osf.io/zn79k/\" target=\"_blank\" rel=\"noopener noreferrer\">preprint\u003c/a> of the study; it's not necessarily the final version of the paper.)Researchers claim AI can be taught to predict sexual orientation from analyzing photographs. LGBTQ advocates are outraged.\u003c/p>\n\u003cp>The research had led to a firestorm of criticism from LGBTQ advocates and academics since it was \u003ca href=\"https://www.economist.com/news/science-and-technology/21728614-machines-read-faces-are-coming-advances-ai-are-used-spot-signs?fsrc=scn/tw/te/bl/ed/advancesinaiareusedtospotsignsofsexuality\" target=\"_blank\" rel=\"noopener noreferrer\">first reported\u003c/a> Sept. 9 by The Economist. Two gay rights groups, Human Rights Campaign and GLAAD, \u003ca href=\"https://www.glaad.org/blog/glaad-and-hrc-call-stanford-university-responsible-media-debunk-dangerous-flawed-report\" target=\"_blank\" rel=\"noopener noreferrer\">called the research,\u003c/a> in a joint press release, \"junk science.\"\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"fullwidth"},"numeric":["fullwidth"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>\u003cstrong>Analyzing Faces\u003c/strong>\u003c/p>\n\u003cp>Using basic facial-recognition technology, the researchers weeded through 130,741 public photos of men and women posted on a U.S. dating website, selecting for images that showed a single face large and clear enough to analyze. This left a pool of 35,326 pictures of 14,776 individuals. Gay and straight people, male and female, were represented evenly.\u003c/p>\n\u003cp>Software called VGG-Face analyzed the faces and looked for correlations between a person's face (nose length, jaw width, etc.) and their self-declared sexual identity on the website. Using a resulting model made up of these distinguishing characteristics, the program, when shown one photo of a gay man and one of a straight man, was able to identify their sexual orientation 81 percent of the time. For women, the success rate was 71 percent. (Accuracy increased when the model was shown more than one image of a person.) Human guessers correctly identified straight faces and gay faces just 61 percent of the time for men and 54 percent for women.\u003c/p>\n\u003cp>The researchers say in the paper that these results \"provide strong support\" for the prenatal hormone theory of gay and lesbian sexual orientation. The theory holds that under or overexposure to prenatal androgens are a key determinant of sexual orientation.\u003c/p>\n\u003cp>In an \u003ca href=\"https://docs.google.com/document/d/11oGZ1Ke3wK9E3BtOFfGfUQuuaSMR8AO2WfWH3aVke6U/preview#\" target=\"_blank\" rel=\"noopener noreferrer\">authors' note\u003c/a> (last updated Sept. 13), the researchers discuss the study's limitations at some length, including the narrow demographic characteristics of the individuals analyzed -- white people who self-reported to be gay or straight. They also expressed concerns about the implications of the study:\u003c/p>\n\u003cblockquote>\u003cp>\u003cspan style=\"font-weight: 400\">We were really disturbed by these results and spent much time considering whether they should be made public at all. We did not want to enable the very risks that we are warning against. \u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400\">Recent press reports,\u003c/span>\u003cspan style=\"font-weight: 400\"> however, suggest that governments and corporations are already using tools aimed at revealing intimate traits from faces. Facial images of billions of people are stockpiled in digital and traditional archives, including dating platforms, photo-sharing websites, and government databases. Profile pictures on Facebook, LinkedIn, and Google Plus are public by default. CCTV cameras and smartphones can be used to take pictures of others’ faces without their permission.\u003c/span>\u003c/p>\u003c/blockquote>\n\u003cp>Critics of the research expressed concerns that it will lead to the very invasion of privacy the authors seek to warn against. HRC/GLAAD also criticized the limited demographic pool used by the researchers, the \"superficial\" nature of the characteristics analyzed in the model, and the way media have represented the study.\u003c/p>\n\u003cp>The \u003ca href=\"https://docs.google.com/document/d/1UuEcSNFMduIaf0cOWdWbOV3NORLoKWdz3big4xuk7Z4/edit#\" target=\"_blank\" rel=\"noopener noreferrer\">authors responded\u003c/a> angrily, calling the HRC/GLAAD press release premature and misleading. \"\u003cspan style=\"font-weight: 400\">They do a great disservice to the LGBTQ community by dismissing our results outright without properly assessing the science behind it, and hurt the mission of the great organizations that they represent,\" they wrote. \u003c/span>\u003c/p>\n\u003cp>The researchers also stressed, in their authors' note, that they did not invent the tools used. Rather, they applied internet-available software to internet-available data, with the goal of demonstrating the privacy risks inherent in artificially intelligent technologies.\u003c/p>\n\u003cp>\"We studied existing technologies,\" wrote Kosinski and Wang, already widely used by companies and governments, to see whether they present a risk to the privacy of LGBTQ individuals.\"\u003c/p>\n\u003cp>They added, \"We were terrified to find that they do.\"\u003c/p>\n\u003cp>Such tools present a special threat, said the authors, to the privacy and safety of gay men and women living under repressive regimes where homosexuality is illegal.\u003c/p>\n\u003cp>But other LGBT academics and writers did not accept this line of reasoning. Oberlin sociology professor Greggor Mattson wrote a \u003ca href=\"https://greggormattson.com/2017/09/09/artificial-intelligence-discovers-gayface/\" target=\"_blank\" rel=\"noopener noreferrer\">takedown,\u003c/a> published on his website, describing the study as \"much less insightful than the researchers claim.\" The authors' discussion of their ethical concerns suffered from \"stunning tone-deafness,\" Mattson wrote.\u003c/p>\n\u003cp>At least one LGBT blogger, though, came to the researchers' defense.\u003c/p>\n\u003cp>Alex Bollinger, writing at LGBTQ Nation, wrote a \u003ca href=\"https://www.lgbtqnation.com/2017/09/hrc-glaad-release-silly-statement-gay-face-study/\" target=\"_blank\" rel=\"noopener noreferrer\">post \u003c/a>titled \"HRC and GLAAD release a silly statement about the ‘gay face’ study.\"\u003c/p>\n\u003cblockquote>\u003cp>\"We should take a stance of curiosity instead of judgment.\u003c/p>\n\u003cp>\"This is just one study that looked at one sample and said a few things. There will be more studies later on that will say other things. Let’s see how that all unfolds before deciding what the correct answer is.\"\u003c/p>\u003c/blockquote>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>This post was edited Oct. 9 to specify the nature of the American Psychological Association's review of the study.\u003c/em>\u003c/p>\n\n\u003c/div>\u003c/p>","attributes":{"named":{},"numeric":[]}}],"link":"/futureofyou/435378/can-facial-recognition-detect-sexual-orientation-controversial-stanford-study-now-under-ethical-review","authors":["11088"],"categories":["futureofyou_452","futureofyou_1","futureofyou_73"],"tags":["futureofyou_849","futureofyou_1253","futureofyou_470"],"featImg":"futureofyou_435379","label":"source_futureofyou_435378"},"futureofyou_435297":{"type":"posts","id":"futureofyou_435297","meta":{"index":"posts_1591205157","site":"futureofyou","id":"435297","score":null,"sort":[1504854079000]},"guestAuthors":[],"slug":"scanning-the-future-radiologists-see-their-jobs-at-risk","title":"Scanning The Future, Radiologists See Their Jobs At Risk","publishDate":1504854079,"format":"standard","headTitle":"KQED Future of You | KQED Science","labelTerm":{"site":"futureofyou"},"content":"\u003cp>In health care, you could say radiologists have typically had a pretty sweet deal. They make, on average, around \u003ca href=\"https://blog.doximity.com/articles/the-first-annual-doximity-physician-compensation-report\">$400,000 a year\u003c/a> — nearly double what a family doctor makes — and often have less grueling hours. But if you talk with radiologists in training at the University of California, San Francisco, it quickly becomes clear that the once-certain golden path is no longer so secure.\u003c/p>\n\u003cp>\"The biggest concern is that we could be replaced by machines,\" says Phelps Kelley, a fourth-year radiology fellow. He's sitting inside a dimly lit reading room, looking at digital images from the CT scan of a patient's chest, trying to figure out why the patient is short of breath.\u003c/p>\n\u003cp>[contextly_sidebar id=\"7vgm3s41JKj4ZsMONu2lh5y7kBqxTICU\"]Because MRI and CT scans are now routine procedures and all the data can be stored digitally, the number of images radiologists have to assess has \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2765780/\">risen dramatically\u003c/a>. These days, a radiologist at UCSF will go through anywhere from 20 to 100 scans a day, and each scan can have thousands of images to review.\u003c/p>\n\u003cp>\"Radiology has become commoditized over the years,\" Kelley says. \"People don't want interaction with a radiologist, they just want a piece of paper that says what the CT shows.\"\u003c/p>\n\u003cp>\"\u003cstrong>Computers are awfully good at seeing patterns\"\u003c/strong>\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>That basic analysis is something he predicts computers will be able to do.\u003c/p>\n\u003cp>Dr. Bob Wachter, an internist at UCSF and author of \u003cem>The Digital Doctor\u003c/em>, says radiology is particularly amenable to takeover by artificial intelligence like machine learning.\u003c/p>\n\u003cp>\"Radiology, at its core, is now a human being, based on learning and his or her own experience, looking at a collection of digital dots and a digital pattern and saying 'That pattern looks like cancer or looks like tuberculosis or looks like pneumonia,' \" he says. \"Computers are awfully good at seeing patterns.\"\u003c/p>\n\u003caside class=\"pullquote alignright\">There is plenty of angst among radiologists today.\u003c/aside>\n\u003cp>Just think about how Facebook software can identify your face in a group photo, or Google's can recognize a stop sign. Big tech companies are betting the same machine learning process — training a computer by feeding it thousands of images — could make it possible for an algorithm to diagnose heart disease or strokes faster and cheaper than a human can.\u003c/p>\n\u003cp>UCSF radiologist Dr. Marc Kohli says there is plenty of angst among radiologists today.\u003c/p>\n\u003cp>\"You can't walk through any of our meetings without hearing people talk about machine learning,\" Kohli says.\u003c/p>\n\u003cp>Both Kohli and his colleague Dr. John Mongan are researching ways to use artificial intelligence in radiology. As part of a UCSF \u003ca href=\"https://www.ucsf.edu/news/2016/11/404956/ucsf-ge-healthcare-launch-deep-learning-partnership-advance-care-globally\">collaboration with GE\u003c/a>, Mongan is helping teach machines to distinguish between normal and abnormal chest X-rays so doctors can prioritize patients with life-threatening conditions. He says the people most fearful about AI understand the least about it. From his office just north of Silicon Valley, he compares the climate to that of the dot-com bubble.\u003c/p>\n\u003cp>\"People were sure about the way things were going to go,\" Mongan says. \"Webvan had billions of dollars and was going to put all the groceries out of business. There's still a Safeway half a mile from my house. But at the same time, it wasn't all hype.\"\u003c/p>\n\u003cp>\"\u003cstrong>You need them working together\"\u003c/strong>\u003c/p>\n\u003cp>The reality is this: Dozens of companies, including IBM, Google and GE, are racing to develop formulas that could one day make diagnoses from medical images. It's not an easy task: to write the complex problem-solving formulas, developers need access to a tremendous amount of health data.\u003c/p>\n\u003cp>\u003ca href=\"https://www.vrad.com/\">Health care companies like vRad\u003c/a>, which has radiologists analyzing 7 million scans a year, provide data to partners that develop medical algorithms.\u003c/p>\n\u003cp>The data has been used to \"create algorithms to detect the risk of acute strokes and hemorrhages\" and help off-site radiologists prioritize their work, says Dr. Benjamin Strong, chief medical officer at vRad.\u003c/p>\n\u003cp>\u003ca href=\"https://www.zebra-med.com/\">Zebra Medical Vision\u003c/a>, an Israeli company, provides algorithms to hospitals across the U.S. that help radiologists predict disease. Chief Medical Officer Eldad Elnekave says computers can detect diseases from images better than humans because they can multitask — say, look for appendicitis while also checking for low bone density.\u003c/p>\n\u003cp>\"The radiologist can't make 30 diagnoses for every study. But the evidence is there, the information is in the pixels,\" Elnekave says.\u003c/p>\n\u003cp>Still, UCSF's Mongan isn't worried about losing his job.\u003c/p>\n\u003cp>\"When we're talking about the machines doing things radiologists can't do, we're not talking about a machine where you can just drop an MRI in it and walk away and the answer gets spit out better than a radiologist,\" he says. \"A CT does things better than a radiologist. But that CT scanner by itself doesn't do much good. You need them working together.\"\u003c/p>\n\u003cfigure id=\"attachment_435300\" class=\"wp-caption alignleft\" style=\"max-width: 800px\">\u003cimg class=\"size-medium wp-image-435300\" src=\"https://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-800x600.jpg\" alt=\"\" width=\"800\" height=\"600\" srcset=\"https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-800x600.jpg 800w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-160x120.jpg 160w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-768x576.jpg 768w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-1020x765.jpg 1020w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-1920x1440.jpg 1920w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-1180x885.jpg 1180w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-960x720.jpg 960w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-240x180.jpg 240w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-375x281.jpg 375w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-520x390.jpg 520w\" sizes=\"(max-width: 800px) 100vw, 800px\">\u003cfigcaption class=\"wp-caption-text\">Radiologist John Mongan is researching ways to use artificial intelligence in radiology. \u003ccite>(Courtesy of Mark Kohli)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cp>In the short term, Mongan is excited that algorithms could help him prioritize patients and make sure he doesn't miss something. Long term, he says radiologists will spend less time looking at images and more time selecting algorithms and interpreting results.\u003c/p>\n\u003cp>Kohli says in addition to embracing artificial intelligence, radiologists need to make themselves more visible by coming out of those dimly lit reading rooms.\u003c/p>\n\u003cp>\"We're largely hidden from the patients,\" Kohli says. \"We're nearly completely invisible, with the exception of my name shows up on a bill, which is a problem.\"\u003c/p>\n\u003cp>Wachter believes increasing collaboration between radiologists and doctors is also critical.\u003c/p>\n\u003cp>\"At UCSF, we're having conversations about [radiologists] coming out of their room and working with us. The more they can become real consultants, I think that will help,\" he says.\u003c/p>\n\u003cp>Kelley, the radiology fellow, says young radiologists who don't shy away from AI will have a far more certain future. His analogy? Uber and the taxi business.\u003c/p>\n\u003cp>\"If the taxi industry had invested in ride-hailing apps maybe they wouldn't be going out of business and Uber wouldn't be taking them over,\" Kelley says. \"So if we can actually own [AI], then we can maybe benefit from it and not be wiped out by it.\"\u003c/p>\n\u003cp>[ad floatright]\u003c/p>\n\u003cp>At least for now, Kelley offers what a computer can't — a diagnosis with a face-to-face explanation.\u003c/p>\n\u003cdiv class=\"fullattribution\">Copyright 2017 KERA. To see more, visit \u003ca href=\"http://www.kera.org/\">KERA\u003c/a>.\u003cimg src=\"https://www.google-analytics.com/__utm.gif?utmac=UA-5828686-4&utmdt=Scanning+The+Future%2C+Radiologists+See+Their+Jobs+At+Risk&utme=8(APIKey)9(MDAxOTAwOTE4MDEyMTkxMDAzNjczZDljZA004)\">\u003c/div>\n\n","blocks":[],"excerpt":"In a new series called 'Is My Job Safe?' NPR looks at the future of jobs at a time of rapid gains in artificial intelligence and robotics. We start with a high-paying job in medicine: radiologists.","status":"publish","parent":0,"modified":1504902355,"stats":{"hasAudio":false,"hasVideo":false,"hasChartOrMap":false,"iframeSrcs":[],"hasGoogleForm":false,"hasGallery":false,"hasHearkenModule":false,"hasPolis":false,"paragraphCount":31,"wordCount":1065},"headData":{"title":"Scanning The Future, Radiologists See Their Jobs At Risk | KQED","description":"In a new series called 'Is My Job Safe?' NPR looks at the future of jobs at a time of rapid gains in artificial intelligence and robotics. We start with a high-paying job in medicine: radiologists.","ogTitle":"","ogDescription":"","ogImgId":"","twTitle":"","twDescription":"","twImgId":"","schema":{"@context":"http://schema.org","@type":"Article","headline":"Scanning The Future, Radiologists See Their Jobs At Risk","datePublished":"2017-09-08T07:01:19.000Z","dateModified":"2017-09-08T20:25:55.000Z","image":"https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"}},"disqusIdentifier":"435297 https://ww2.kqed.org/futureofyou/?p=435297","disqusUrl":"https://ww2.kqed.org/futureofyou/2017/09/08/scanning-the-future-radiologists-see-their-jobs-at-risk/","disqusTitle":"Scanning The Future, Radiologists See Their Jobs At Risk","nprImageCredit":"xijian","nprByline":"Lauren Silverman\u003c/BR>NPR All Tech Considered","nprImageAgency":"iStockphoto","nprStoryId":"547882005","nprApiLink":"http://api.npr.org/query?id=547882005&apiKey=MDAxOTAwOTE4MDEyMTkxMDAzNjczZDljZA004","nprHtmlLink":"http://www.npr.org/sections/alltechconsidered/2017/09/04/547882005/scanning-the-future-radiologists-see-their-jobs-at-risk?ft=nprml&f=547882005","nprRetrievedStory":"1","nprPubDate":"Tue, 05 Sep 2017 15:59:00 -0400","nprStoryDate":"Mon, 04 Sep 2017 16:50:00 -0400","nprLastModifiedDate":"Tue, 05 Sep 2017 15:59:28 -0400","nprAudio":"https://ondemand.npr.org/anon.npr-mp3/npr/atc/2017/09/20170904_atc_scanning_the_future_radiologists_see_their_jobs_at_risk.mp3?orgId=77&topicId=1019&d=281&p=2&story=547882005&t=progseg&e=548407189&seg=19&ft=nprml&f=547882005","nprAudioM3u":"http://api.npr.org/m3u/1548505877-d491ad.m3u?orgId=77&topicId=1019&d=281&p=2&story=547882005&t=progseg&e=548407189&seg=19&ft=nprml&f=547882005","path":"/futureofyou/435297/scanning-the-future-radiologists-see-their-jobs-at-risk","audioUrl":"https://ondemand.npr.org/anon.npr-mp3/npr/atc/2017/09/20170904_atc_scanning_the_future_radiologists_see_their_jobs_at_risk.mp3?orgId=77&topicId=1019&d=281&p=2&story=547882005&t=progseg&e=548407189&seg=19&ft=nprml&f=547882005","audioTrackLength":null,"parsedContent":[{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003cp>In health care, you could say radiologists have typically had a pretty sweet deal. They make, on average, around \u003ca href=\"https://blog.doximity.com/articles/the-first-annual-doximity-physician-compensation-report\">$400,000 a year\u003c/a> — nearly double what a family doctor makes — and often have less grueling hours. But if you talk with radiologists in training at the University of California, San Francisco, it quickly becomes clear that the once-certain golden path is no longer so secure.\u003c/p>\n\u003cp>\"The biggest concern is that we could be replaced by machines,\" says Phelps Kelley, a fourth-year radiology fellow. He's sitting inside a dimly lit reading room, looking at digital images from the CT scan of a patient's chest, trying to figure out why the patient is short of breath.\u003c/p>\n\u003cp>\u003c/p>\u003cp>\u003c/p>\u003cp>Because MRI and CT scans are now routine procedures and all the data can be stored digitally, the number of images radiologists have to assess has \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2765780/\">risen dramatically\u003c/a>. These days, a radiologist at UCSF will go through anywhere from 20 to 100 scans a day, and each scan can have thousands of images to review.\u003c/p>\n\u003cp>\"Radiology has become commoditized over the years,\" Kelley says. \"People don't want interaction with a radiologist, they just want a piece of paper that says what the CT shows.\"\u003c/p>\n\u003cp>\"\u003cstrong>Computers are awfully good at seeing patterns\"\u003c/strong>\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"fullwidth"},"numeric":["fullwidth"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>That basic analysis is something he predicts computers will be able to do.\u003c/p>\n\u003cp>Dr. Bob Wachter, an internist at UCSF and author of \u003cem>The Digital Doctor\u003c/em>, says radiology is particularly amenable to takeover by artificial intelligence like machine learning.\u003c/p>\n\u003cp>\"Radiology, at its core, is now a human being, based on learning and his or her own experience, looking at a collection of digital dots and a digital pattern and saying 'That pattern looks like cancer or looks like tuberculosis or looks like pneumonia,' \" he says. \"Computers are awfully good at seeing patterns.\"\u003c/p>\n\u003caside class=\"pullquote alignright\">There is plenty of angst among radiologists today.\u003c/aside>\n\u003cp>Just think about how Facebook software can identify your face in a group photo, or Google's can recognize a stop sign. Big tech companies are betting the same machine learning process — training a computer by feeding it thousands of images — could make it possible for an algorithm to diagnose heart disease or strokes faster and cheaper than a human can.\u003c/p>\n\u003cp>UCSF radiologist Dr. Marc Kohli says there is plenty of angst among radiologists today.\u003c/p>\n\u003cp>\"You can't walk through any of our meetings without hearing people talk about machine learning,\" Kohli says.\u003c/p>\n\u003cp>Both Kohli and his colleague Dr. John Mongan are researching ways to use artificial intelligence in radiology. As part of a UCSF \u003ca href=\"https://www.ucsf.edu/news/2016/11/404956/ucsf-ge-healthcare-launch-deep-learning-partnership-advance-care-globally\">collaboration with GE\u003c/a>, Mongan is helping teach machines to distinguish between normal and abnormal chest X-rays so doctors can prioritize patients with life-threatening conditions. He says the people most fearful about AI understand the least about it. From his office just north of Silicon Valley, he compares the climate to that of the dot-com bubble.\u003c/p>\n\u003cp>\"People were sure about the way things were going to go,\" Mongan says. \"Webvan had billions of dollars and was going to put all the groceries out of business. There's still a Safeway half a mile from my house. But at the same time, it wasn't all hype.\"\u003c/p>\n\u003cp>\"\u003cstrong>You need them working together\"\u003c/strong>\u003c/p>\n\u003cp>The reality is this: Dozens of companies, including IBM, Google and GE, are racing to develop formulas that could one day make diagnoses from medical images. It's not an easy task: to write the complex problem-solving formulas, developers need access to a tremendous amount of health data.\u003c/p>\n\u003cp>\u003ca href=\"https://www.vrad.com/\">Health care companies like vRad\u003c/a>, which has radiologists analyzing 7 million scans a year, provide data to partners that develop medical algorithms.\u003c/p>\n\u003cp>The data has been used to \"create algorithms to detect the risk of acute strokes and hemorrhages\" and help off-site radiologists prioritize their work, says Dr. Benjamin Strong, chief medical officer at vRad.\u003c/p>\n\u003cp>\u003ca href=\"https://www.zebra-med.com/\">Zebra Medical Vision\u003c/a>, an Israeli company, provides algorithms to hospitals across the U.S. that help radiologists predict disease. Chief Medical Officer Eldad Elnekave says computers can detect diseases from images better than humans because they can multitask — say, look for appendicitis while also checking for low bone density.\u003c/p>\n\u003cp>\"The radiologist can't make 30 diagnoses for every study. But the evidence is there, the information is in the pixels,\" Elnekave says.\u003c/p>\n\u003cp>Still, UCSF's Mongan isn't worried about losing his job.\u003c/p>\n\u003cp>\"When we're talking about the machines doing things radiologists can't do, we're not talking about a machine where you can just drop an MRI in it and walk away and the answer gets spit out better than a radiologist,\" he says. \"A CT does things better than a radiologist. But that CT scanner by itself doesn't do much good. You need them working together.\"\u003c/p>\n\u003cfigure id=\"attachment_435300\" class=\"wp-caption alignleft\" style=\"max-width: 800px\">\u003cimg class=\"size-medium wp-image-435300\" src=\"https://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-800x600.jpg\" alt=\"\" width=\"800\" height=\"600\" srcset=\"https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-800x600.jpg 800w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-160x120.jpg 160w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-768x576.jpg 768w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-1020x765.jpg 1020w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-1920x1440.jpg 1920w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-1180x885.jpg 1180w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-960x720.jpg 960w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-240x180.jpg 240w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-375x281.jpg 375w, https://ww2.kqed.org/app/uploads/sites/13/2017/09/john_mongan-8ccf101ed85390d31991946b2b4b74dcd51e5960-520x390.jpg 520w\" sizes=\"(max-width: 800px) 100vw, 800px\">\u003cfigcaption class=\"wp-caption-text\">Radiologist John Mongan is researching ways to use artificial intelligence in radiology. \u003ccite>(Courtesy of Mark Kohli)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cp>In the short term, Mongan is excited that algorithms could help him prioritize patients and make sure he doesn't miss something. Long term, he says radiologists will spend less time looking at images and more time selecting algorithms and interpreting results.\u003c/p>\n\u003cp>Kohli says in addition to embracing artificial intelligence, radiologists need to make themselves more visible by coming out of those dimly lit reading rooms.\u003c/p>\n\u003cp>\"We're largely hidden from the patients,\" Kohli says. \"We're nearly completely invisible, with the exception of my name shows up on a bill, which is a problem.\"\u003c/p>\n\u003cp>Wachter believes increasing collaboration between radiologists and doctors is also critical.\u003c/p>\n\u003cp>\"At UCSF, we're having conversations about [radiologists] coming out of their room and working with us. The more they can become real consultants, I think that will help,\" he says.\u003c/p>\n\u003cp>Kelley, the radiology fellow, says young radiologists who don't shy away from AI will have a far more certain future. His analogy? Uber and the taxi business.\u003c/p>\n\u003cp>\"If the taxi industry had invested in ride-hailing apps maybe they wouldn't be going out of business and Uber wouldn't be taking them over,\" Kelley says. \"So if we can actually own [AI], then we can maybe benefit from it and not be wiped out by it.\"\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"floatright"},"numeric":["floatright"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>At least for now, Kelley offers what a computer can't — a diagnosis with a face-to-face explanation.\u003c/p>\n\u003cdiv class=\"fullattribution\">Copyright 2017 KERA. To see more, visit \u003ca href=\"http://www.kera.org/\">KERA\u003c/a>.\u003cimg src=\"https://www.google-analytics.com/__utm.gif?utmac=UA-5828686-4&utmdt=Scanning+The+Future%2C+Radiologists+See+Their+Jobs+At+Risk&utme=8(APIKey)9(MDAxOTAwOTE4MDEyMTkxMDAzNjczZDljZA004)\">\u003c/div>\n\n\u003c/div>\u003c/p>","attributes":{"named":{},"numeric":[]}}],"link":"/futureofyou/435297/scanning-the-future-radiologists-see-their-jobs-at-risk","authors":["byline_futureofyou_435297"],"categories":["futureofyou_1"],"tags":["futureofyou_849","futureofyou_1275","futureofyou_1104"],"featImg":"futureofyou_435298","label":"futureofyou"},"futureofyou_435315":{"type":"posts","id":"futureofyou_435315","meta":{"index":"posts_1591205157","site":"futureofyou","id":"435315","score":null,"sort":[1504722796000]},"guestAuthors":[],"slug":"ibm-pitched-its-watson-supercomputer-as-a-revolution-in-cancer-care-its-nowhere-close","title":"IBM Pitched Its Watson Supercomputer as a Revolution in Cancer Care. It’s Nowhere Close","publishDate":1504722796,"format":"standard","headTitle":"KQED Future of You | KQED Science","labelTerm":{"term":1097,"site":"futureofyou"},"content":"\u003cp>It was an audacious undertaking, even for one of the most storied American companies: With a single machine, IBM would tackle humanity’s most vexing diseases and revolutionize medicine.\u003c/p>\n\u003cp>Breathlessly promoting its signature brand — Watson — IBM sought to capture the world’s imagination, and it quickly zeroed in on a high-profile target: cancer.\u003c/p>\n\u003cp>But three years after IBM began selling Watson to recommend the best cancer treatments to doctors around the world, a STAT investigation has found that the supercomputer isn’t living up to the lofty expectations IBM created for it. It is still struggling with the basic step of learning about different forms of cancer. Only a few dozen hospitals have adopted the system, which is a long way from IBM’s goal of establishing dominance in a multibillion-dollar market. And at foreign hospitals, physicians complained its advice is biased toward American patients and methods of care.\u003c/p>\n\u003caside class=\"pullquote alignright\">IBM has not exposed the product to critical review by outside scientists or conducted clinical trials to assess its effectiveness.\u003c/aside>\n\u003cp>STAT examined Watson for Oncology’s use, marketing, and performance in hospitals across the world, from South Korea to Slovakia to South Florida. Reporters interviewed dozens of doctors, IBM executives, artificial intelligence experts, and others familiar with the system’s underlying technology and rollout.\u003c/p>\n\u003cp>The interviews suggest that IBM, in its rush to bolster flagging revenue, unleashed a product without fully assessing the challenges of deploying it in hospitals globally. While it has \u003ca href=\"https://www.youtube.com/watch?v=au4kzyJUlrA\" target=\"_blank\" rel=\"noopener noreferrer\">emphatically marketed\u003c/a> Watson for cancer care, IBM hasn’t published any scientific papers demonstrating how the technology affects physicians and patients. As a result, its flaws are getting exposed on the front lines of care by doctors and researchers who say that the system, while promising in some respects, remains undeveloped.\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>“Watson for Oncology is in their toddler stage, and we have to wait and actively engage, hopefully to help them grow healthy,” said Dr. Taewoo Kang, a South Korean cancer specialist who has used the product.\u003c/p>\n\u003cp>At its heart, Watson for Oncology uses the cloud-based supercomputer to digest massive amounts of data — from doctor’s notes to medical studies to clinical guidelines. But its treatment recommendations are not based on its own insights from these data. Instead, they are based exclusively on training by human overseers, who laboriously feed Watson information about how patients with specific characteristics should be treated.\u003c/p>\n\u003cp>IBM executives acknowledged \u003ca href=\"https://www.ibm.com/watson/health/oncology-and-genomics/oncology/\" target=\"_blank\" rel=\"noopener noreferrer\">Watson for Oncology\u003c/a>, which has been in development for nearly six years, is in its infancy. But they said it is improving rapidly, noting that by year’s end, the system will offer guidance about treatment for 12 cancers that account for 80 percent of the world’s cases. They said it’s saving doctors time and ensuring that patients get top-quality care.\u003c/p>\n\u003cp>“We’re seeing stories come in where patients are saying, ‘It gave me peace of mind,’” Watson Health general manager Deborah DiSanzo said. “That makes us feel extraordinarily good that what we’re doing is going to make a difference for patients and their physicians.”\u003c/p>\n\u003cp>But contrary to IBM’s depiction of Watson as a digital prodigy, the supercomputer’s abilities are limited.\u003c/p>\n\u003cp>Perhaps the most stunning overreach is in the company’s claim that Watson for Oncology, through artificial intelligence, can sift through reams of data to generate new insights and identify, as an IBM sales rep put it, “even new approaches” to cancer care. STAT found that the system doesn’t create new knowledge and is artificially intelligent only in the most rudimentary sense of the term.\u003c/p>\n\u003cp>While Watson became a household name by winning the TV game show “Jeopardy!”, its programming is akin to a different game-playing machine: the Mechanical Turk, a chess-playing robot of the 1700s, which dazzled audiences but hid a secret — a human operator shielded inside.\u003c/p>\n\u003cp>In the case of Watson for Oncology, those human operators are a couple dozen physicians at a single, though highly respected, U.S. hospital: Memorial Sloan Kettering Cancer Center in New York. Doctors there are empowered to input their own recommendations into Watson, even when the evidence supporting those recommendations is thin.\u003c/p>\n\u003cp>The actual capabilities of Watson for Oncology are not well-understood by the public, and even by some of the hospitals that use it. It’s taken nearly six years of painstaking work by data engineers and doctors to train Watson in just seven types of cancer, and keep the system updated with the latest knowledge.\u003c/p>\n\u003cp>“It’s been a struggle to update, I’ll be honest,” said Dr. Mark Kris, Memorial Sloan Kettering’s lead Watson trainer. He noted that treatment guidelines for every metastatic lung cancer patient worldwide recently changed in the course of one week after a research presentation at a cancer conference. “Changing the system of cognitive computing doesn’t turn around on a dime like that,” he said. “You have to put in the literature, you have to put in cases.”\u003c/p>\n\u003cp>Watson grew out of an effort to transform IBM from an old-guard hardware company to one that operates in the cloud and along the cutting edge of artificial intelligence. Despite its use in an array of industries — from banking to manufacturing — it has failed to end a streak of 21 consecutive quarters of declining revenue at IBM. In the most recent quarter, revenue even slid from the same period last year in IBM’s cognitive solutions division — which is built around Watson and is supposed to be the future of its business.\u003c/p>\n\u003cp>[contextly_sidebar id=\"Z4v10mXX1qddzeH6LkJrk48aWafwbS4D\"]In response to STAT’s questions, IBM said Watson, in health care and otherwise, remains on an upward trajectory and “is already an important part” of its $20 billion analytics business. Health care is a crucial part of the Watson enterprise. IBM employs 7,000 people in its Watson health division and sees the industry as a \u003ca href=\"https://www.ibm.com/investor/att/pdf/2017_Investor_Briefing_Financial_Discussion_charts.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">$200 billion market\u003c/a> over the next several years. Only financial services, at $300 billion, is considered a bigger opportunity by the company.\u003c/p>\n\u003cp>At stake in the supercomputer’s performance is not just the fortunes of a famed global company. In the world of medicine, Watson is also something of a digital canary — the most visible attempt to use artificial intelligence to identify the best ways to prevent and treat disease. The system’s larger goal, IBM executives say, is to democratize medical knowledge so that every patient, no matter the person’s geography or income level, will be able to access the best care.\u003c/p>\n\u003cp>But in cancer treatment, the pursuit of that utopian ideal has faltered.\u003c/p>\n\u003cp>STAT’s investigation focused on Watson for Oncology because that product is the furthest along in clinical care, though Watson sells separate packages to analyze genomic information and match patients to clinical trials. It’s also applying Watson to other tasks, including honing \u003ca href=\"https://www.ibm.com/watson/health/value-based-care/population-health-management/\" target=\"_blank\" rel=\"noopener noreferrer\">preventive medicine practices\u003c/a> and reading \u003ca href=\"https://www.ibm.com/watson/health/imaging/\" target=\"_blank\" rel=\"noopener noreferrer\">medical images\u003c/a>.\u003c/p>\n\u003cp>Doctors’ reliance on Watson for Oncology varies among hospitals. While institutions with fewer specialists lean more heavily on its recommendations, others relegate the system to a background role, like a paralegal whose main skill is researching existing knowledge.\u003c/p>\n\u003cp>[contextly_sidebar id=\"AqsguTTnUCDkh8FM62DUnHUeMNt65aa2\"]Hospitals pay a per-patient fee for Watson for Oncology and other products enabled by the supercomputer. The amount depends on the number of products a hospital buys, and ranges between $200 and $1,000 per patient, according to DiSanzo. The system sometimes comes with consulting costs and is expensive to link with electronic medical records. At hospitals that don’t link it with their medical records, more time must be spent typing in patient information.\u003c/p>\n\u003cp>At Jupiter Medical Center in Florida, that task falls to nurse Jean Thompson, who spends about 90 minutes a week feeding data into the machine. Once she has completed that work, she clicks the “Ask Watson” button to get the supercomputer’s advice for treating patients.\u003c/p>\n\u003cp>On a recent morning, the results for a 73-year-old lung cancer patient were underwhelming: Watson recommended a chemotherapy regimen the oncologists had already flagged.\u003c/p>\n\u003cp>“It’s fine,” Dr. Sujal Shah, a medical oncologist, said of Watson’s treatment suggestion while discussing the case with colleagues.\u003c/p>\n\u003cp>He said later that the background information Watson provided, including medical journal articles, was helpful, giving him more confidence that using a specific chemotherapy was a sound idea. But the system did not directly help him make that decision, nor did it tell him anything he didn’t already know.\u003c/p>\n\u003cp>Jupiter is one of two U.S. hospitals that have adopted Watson for Oncology. The system has generated more business in India and Southeast Asia. Many doctors in those countries said Watson is saving time and helping more patients get quality care. But they also said its accuracy and overall value is limited by differing medical practices and economic circumstances.\u003c/p>\n\u003cp>Despite IBM’s marketing blitz, with years of high-profile Watson commercials featuring celebrities from Serena Williams to Bob Dylan to Jon Hamm, the company’s executives are not always gushing. In interviews with STAT, they acknowledged the system faces challenges and needs better integration with electronic medical records and more data on real patients to find patterns and suggest cutting-edge treatments.\u003c/p>\n\u003cp>“The goal as Watson gets smarter is for it to make some of those recommendations in a more automated way, to sort of suggest now may be the time and let us flip the switch” when a promising treatment option emerges, said Dr. Andrew Norden, a former IBM deputy health chief who left the company in early August. “As I describe it, you’re probably getting a sense it’s really hard and nuanced.”\u003c/p>\n\u003cp>Such nuance is absent from the careful narrative IBM has constructed to sell Watson.\u003c/p>\n\u003cp>https://www.youtube.com/watch?v=UpFHNGF4F8o\u003c/p>\n\u003cp>It is by design that there is not one independent, third-party study that examines whether Watson for Oncology can deliver. IBM has not exposed the product to critical review by outside scientists or conducted clinical trials to assess its effectiveness.\u003c/p>\n\u003cp>While it’s not unheard of for companies to avoid external vetting early on, IBM’s circumstances are unusual because Watson for Oncology is not in development — it has already been deployed around the world.\u003c/p>\n\u003cp>Yoon Sup Choi, a South Korean venture capitalist and researcher who wrote a book about artificial intelligence in health care, said IBM isn’t required by regulatory agencies to do a clinical trial in South Korea or America before selling the system to hospitals. And given that hospitals are already using the system, a clinical trial would be unlikely to improve business prospects.\u003c/p>\n\u003cp>“It’s too risky, right?” Choi said. “If the result of the clinical trial is not very good — [if] there’s a marginal clinical benefit from Watson — it’s really bad news to the whole IBM.”\u003c/p>\n\u003cp>Pilar Ossorio, a professor of law and bioethics at University of Wisconsin Law School, said Watson should be subject to tighter regulation because of its role in treating patients. “As an ethical matter, and as a scientific matter, you should have to prove that there’s safety and efficacy before you can just go do this,” she said.\u003c/p>\n\u003caside class=\"pullquote alignright\">'Artificial intelligence will be adopted in all medical fields in the future. If that trend, that change is inevitable, then why don’t we just start early?'\u003ccite> Dr. Uhn Lee, Watson program, Gachon University Gil Medical Center, South Korea\u003c/cite>\u003c/aside>\n\u003cp>Norden dismissed the suggestion IBM should have been required to conduct a clinical trial before commercializing Watson, noting that many practices in medicine are widely accepted even though they aren’t supported by a randomized controlled trial.\u003c/p>\n\u003cp>“Has there ever been a randomized trial of parachutes for paratroopers?” Norden asked. “And the answer is, of course not, because there is a very strong intuitive value proposition. … So I believe that bringing the best information to bear on medical decision making is a no-brainer.”\u003c/p>\n\u003cp>IBM said in its statement that it has collaborated with the research community and presented data on Watson at industry gatherings and in peer-reviewed journals. Some doctors said they didn’t need to see more research to know that the system is valuable. “Artificial intelligence will be adopted in all medical fields in the future,” said Dr. Uhn Lee, who runs the Watson program at Gachon University Gil Medical Center in South Korea. “If that trend, that change is inevitable, then why don’t we just start early?”\u003c/p>\n\u003cp>So far, the only studies about Watson for Oncology are conference abstracts. The full results haven’t been published in peer-reviewed journals — and every study, save one, was either conducted by a paying customer or included IBM staff on the author list, or both. Most trumpet positive results, showing that Watson saves doctors time and has a high concordance rate with their treatment recommendations.\u003c/p>\n\u003cp>The “concordance” studies comprise the vast majority of the public research on Watson for Oncology. Doctors will ask Watson for its advice for treating a slew of patients, and then compare its recommendations to those of oncologists. In an unpublished study from Denmark, the rate of agreement was about 33 percent — so the hospital decided not to buy the system. In other countries, the rate can be as high as \u003ca href=\"http://meetinglibrary.asco.org/record/145389/abstract\" target=\"_blank\" rel=\"noopener noreferrer\">96 percent\u003c/a> for some cancers. But showing that Watson agrees with the doctors proves only that it is competent in applying existing methods of care, not that it can improve them.\u003c/p>\n\u003cp>IBM executives said they are pursuing studies to examine the impact on doctors and patients, although none has been completed to date.\u003c/p>\n\u003cp>Questions about Watson have begun spilling into public view, including in a \u003ca href=\"http://gizmodo.com/why-everyone-is-hating-on-watson-including-the-people-w-1797510888\" target=\"_blank\" rel=\"noopener noreferrer\">recent Gizmodo story\u003c/a> headlined “Why Everyone is Hating on IBM Watson — Including the People Who Helped Make It.” The most prominent failure occurred last February when MD Anderson Cancer Center, part of the University of Texas, cancelled its partnership with Watson.\u003c/p>\n\u003cp>The MD Anderson alliance was essentially the \u003ca href=\"http://www.washingtonpost.com/sf/national/2015/06/27/watsons-next-feat-taking-on-cancer/?utm_term=.ada22b3eefb7\" target=\"_blank\" rel=\"noopener noreferrer\">early face\u003c/a> of Watson in health care. The Houston hospital was among IBM’s first partners, and it was using the system to create its own expert oncology adviser, similar to the one IBM was developing with Memorial Sloan Kettering. But the project disintegrated amid internal allegations of overspending, delays, and mismanagement. In all, MD Anderson spent more than three years and $60 million — much of it on outside consultants — before shelving the effort.\u003c/p>\n\u003cp>The hospital declined to answer questions. But the project leader, Dr. Lynda Chin, in her first media interview on the subject, told STAT about the challenges she faced. Chin left MD Anderson before the project collapsed; a subsequent audit flagged several violations of procurement rules under her leadership.\u003c/p>\n\u003caside class=\"pullquote alignright\">How do we ensure the most important tenet in medicine: Do no harm?\u003c/aside>\n\u003cp>Chin said that Watson is a powerful technology, but that it is exceedingly difficult to make functional in health care. She and her team encountered numerous roadblocks, some of which still have not been fully addressed by IBM — at MD Anderson or elsewhere.\u003c/p>\n\u003cp>The cancer hospital’s first major challenge involved getting the machine to deal with the idiosyncrasies of medical records: the acronyms, human errors, shorthand phrases, and different styles of writing. “Teaching a machine to read a record is a lot harder than anyone thought,” she said. Her team spent countless hours on that problem, trying to get Watson to extract valuable information from medical records so that it could apply them to its recommendations.\u003c/p>\n\u003cp>Chin said her team also wrestled with deploying the system in clinical practice. Watson, even if guided by doctors, is as close as medicine has ever gotten to allowing a machine to help decide the treatments delivered to human beings. That carries with it thorny questions, such as how to test the safety of a digital treatment adviser, how to ensure its compliance with regulations, and how to incorporate it into the daily work of doctors and nurses.\u003c/p>\n\u003cp>“Importantly,” Chin said. “How do we create an environment that can ensure the most important tenet in medicine: Do no harm?”\u003c/p>\n\u003cp>Finally, the project ran into a bigger obstacle: Even if you can get Watson to understand patient variables and make competent treatment recommendations, how do you get it access to enough patient data, from enough different sources, to derive insights that could significantly advance the standard of care?\u003c/p>\n\u003cp>Chin said that was a showstopper. Watson did not have a connected network of institutions feeding data about specific cohorts of patients. “You may have 10,000 patients for lung cancer. That is still not a very big number when you think about it,” she said.\u003c/p>\n\u003cp>With data from many more patients, Chin said, you could see patterns — “subsets [of patients] that respond a certain way, subsets that don’t, subsets that have a certain toxicity. That pattern would help with better personalized and precision medicine. But we can’t get there without the ability to actually have a way of aggregating them.”\u003c/p>\n\u003cp>IBM told STAT that Chin’s work was separate from the effort to create Watson for Oncology, which was validated by cancer specialists at Memorial Sloan Kettering prior to its deployment. The company said that Watson for Oncology can extract and summarize substantial text from patient records, though the information must be verified by a clinician, and that it has made significant progress in obtaining more data to improve Watson’s performance. It pointed to partnerships with the health care publisher Elsevier and the analytics firm \u003ca href=\"https://www-03.ibm.com/press/us/en/pressrelease/47031.wss\" target=\"_blank\" rel=\"noopener noreferrer\">Doctor Evidence\u003c/a>.\u003c/p>\n\u003cp>To date, more than 50 hospitals on five continents have agreements with IBM, or intermediary technology companies, to use Watson for Oncology to treat patients, and others are using the genomics and clinical trials products.\u003c/p>\n\u003cp>But the partnership with Memorial Sloan Kettering, and the product that grew out of it, resulted in complications that IBM has papered over with carefully parsed statements and misleading marketing.\u003c/p>\n\u003cp>In its press releases, IBM celebrates \u003ca href=\"https://www.mskcc.org/about/innovative-collaborations/watson-oncology\" target=\"_blank\" rel=\"noopener noreferrer\">Memorial Sloan Kettering’s role\u003c/a> as the only trainer of Watson. After all, who better to educate the system than doctors at one of the world’s most renowned cancer hospitals?\u003c/p>\n\u003cp>But several doctors said Memorial Sloan Kettering’s training injects bias into the system, because the treatment recommendations it puts into Watson don’t always comport with the practices of doctors elsewhere in the world.\u003c/p>\n\u003cp>Given the same clinical scenario, doctors can — and often do — disagree about the best course of action, whether to recommend surgery or chemotherapy, or another treatment. Those discrepancies are especially wide for second- and third-line treatments given after an initial therapy fails, where evidence of benefits is slimmer and consensus more elusive.\u003c/p>\n\u003cp>Rather than acknowledge this dilemma, IBM executives, in marketing materials and interviews, have sought to downplay it. In an interview with STAT, DiSanzo, the head of Watson Health, rejected the idea that Memorial Sloan Kettering’s involvement creates any bias at all.\u003c/p>\n\u003cp>“The bias is taken out by the sheer amount of data we have,” she said, referring to patient cases and millions of articles and studies fed into Watson.\u003c/p>\n\u003cp>But that mischaracterizes how Watson for Oncology works. (IBM later claimed that DiSanzo was referring to Watson in general.)\u003c/p>\n\u003cp>The system is essentially Memorial Sloan Kettering in a portable box. Its treatment recommendations are based entirely on the training provided by doctors, who determine what information Watson needs to devise its guidance as well as what those recommendations should be.\u003c/p>\n\u003cp>When users ask Watson for advice, the system also searches published literature — some of which is curated by Memorial Sloan Kettering — to provide relevant studies and background information to support its recommendation. But the recommendation itself is derived from the training provided by the hospital’s doctors, not the outside literature.\u003c/p>\n\u003cp>Doctors at Memorial Sloan Kettering acknowledged their influence on Watson. “We are not at all hesitant about inserting our bias, because I think our bias is based on the next best thing to prospective randomized trials, which is having a vast amount of experience,” said Dr. Andrew Seidman, one of the hospital’s lead trainers of Watson. “So it’s a very unapologetic bias.”\u003c/p>\n\u003cp>Seidman said the hospital is careful to keep its training grounded in clinical evidence when the evidence exists, but it is not shy about giving its recommendations when it doesn’t. “We want cancer care to be democratized,” he said. “We don’t want doctors who don’t have the thousands and thousands of patients’ experience on a more rare cancer to be handicapped. We want to share that knowledge base.”\u003c/p>\n\u003cp>At a recent training session of Watson on Manhattan’s Upper East Side, the tensions involved in programming the system were on full display. STAT sat in as Memorial Sloan Kettering doctors, led by Seidman, gathered with IBM engineers to train Watson to treat bladder cancer. Five IBM engineers sat on one side of the table. Across from them were three oncologists — one specializing in surgery, another in radiation, and a third in chemotherapy and targeted medicines.\u003c/p>\n\u003cp>Several minutes into the discussion, the question arose of which treatment to recommend for patients whose cancers persisted through six rounds of chemotherapy. The options in such cases tend to be as slim as the evidence supporting them. Should Watson recommend a radical surgery to remove the bladder? Dr. Tim Donahue, the surgical oncologist, noted that such surgery seldom cures patients and is not associated with improved survival in his experience.\u003c/p>\n\u003cp>Then what about another course of chemotherapy combined with radiation?\u003c/p>\n\u003cp>When Watson gives its recommendations, it puts the top recommendation in green, alternative options in orange, and not recommended options in red.\u003c/p>\n\u003cp>But in some clinical scenarios, it’s difficult to tell the colors apart.\u003c/p>\n\u003cp>“This is the hard part of this whole game,” Dr. Marisa Kollmeier, the radiation oncologist, said during the training. “There’s a lack of evidence. And you don’t know if something should be in green without evidence. We don’t have a randomized trial to support every decision.”\u003c/p>\n\u003cp>But the task in front of them required the doctors to press ahead. And they did, rifling through an array of clinical scenarios. In some cases, a large body of evidence backed up their answers. But many others fell into a gray area or were clouded by the inevitable uncertainty of patient preferences.\u003c/p>\n\u003cp>The meeting was one of many in a months-long process to bring Watson up to speed in bladder cancer. Subsequent sessions would involve feeding it data on real patient cases at Memorial Sloan Kettering, so doctors could reinforce Watson’s training with repetition.\u003c/p>\n\u003cp>That training does not teach Watson to base its recommendations on the outcomes of these patients, whether they lived, or died or survived longer than similar patients. Rather, Watson makes its recommendations based on the treatment preferences of Memorial Sloan Kettering physicians.\u003c/p>\n\u003cp>At some institutions using Watson, IBM’s lack of clarity on the cancer center’s role causes confusion. Some seem to think they are getting advice from doctors around the world.\u003c/p>\n\u003cp>“As we tell the patients, it’s like another consultation, but it’s a worldwide consultation,” said Dr. K. Adam Lee, medical director of thoracic oncology at Jupiter Medical Center, when STAT visited in June.\u003c/p>\n\u003caside class=\"pullquote alignright\">Oncologists at one hospital said they have dropped the project altogether after finding that local doctors agreed with Watson in only about 33 percent of cases.\u003c/aside>\n\u003cp>“Really worldwide,” added Kerri Ward, an oncology nurse at the hospital. “It pulls from 300 journals, just for oncology, the clinical database, so the national clinical database, journals, textbooks, and then Sloan Kettering is the one that’s feeding in the clinical [information] currently.”\u003c/p>\n\u003cp>Robert Garrett, the CEO of Hackensack Meridian Health, a group in New Jersey that is using a version of Watson for Oncology, said the information in Watson is “global.”\u003c/p>\n\u003cp>“If you’re a patient that has colon cancer, they have in their database, as I understand it, how colon cancer is treated around the world, by different clinicians, what’s been the most effective treatment for different phases of colon cancer,” Garrett said. “That’s what IBM Watson brings to the table.”\u003c/p>\n\u003cp>None of that accurately depicts how Watson for Oncology works.\u003c/p>\n\u003cp>Several doctors who have examined Watson in other countries told STAT that Memorial Sloan Kettering’s role has given them pause. Researchers in Denmark and the Netherlands said hospitals in their countries have not signed on with Watson because it is too focused on the preferences of a few American doctors.\u003c/p>\n\u003cp>Martijn van Oijen, an epidemiologist and associate professor at Academic Medical Center in the Netherlands, said Memorial Sloan Kettering is packed with top specialists but doesn’t have a monopoly on cancer expertise. “The bad thing is, it’s a U.S.-based hospital with a different approach than some other hospitals in the world,” said van Oijen, who’s involved in a national initiative to evaluate technologies like Watson and is a strong believer in using artificial intelligence to help cancer doctors.\u003c/p>\n\u003cp>In Denmark, oncologists at one hospital said they have dropped the project altogether after finding that local doctors agreed with Watson in only about 33 percent of cases.\u003c/p>\n\u003cp>“We had a discussion with [IBM] that they had a very limited view on the international literature, basically, putting too much stress on American studies, and too little stress on big, international, European, and other-part-of-the-world studies,” said Dr. Leif Jensen, who directs the center at Rigshospitalet in Copenhagen that contains the oncology department.\u003c/p>\n\u003cp>In countries where doctors were trained in the United States, or they use similar treatment guidelines as the Memorial Sloan Kettering doctors, Watson for Oncology can be helpful. Taiwan uses the same guidelines as Americans, so Watson’s advice will be useful there, said Dr. Jeng-Fong Chiou, vice superintendent of the Taipei Cancer Center at Taipei Medical University, which started using Watson for Oncology with patients in July.\u003c/p>\n\u003cp>But he also said there are differences between American and Taiwanese patients — his patients often receive lower doses of drugs to minimize side effects — and that his oncologists will have to make adjustments from Watson’s recommendations.\u003c/p>\n\u003cp>The generally affluent population treated at Memorial Sloan Kettering doesn’t reflect the diversity of people around the world. The cases used to train Watson therefore don’t take into account the economic and social issues faced by patients in poorer countries, noted Ossorio, the University of Wisconsin law professor.\u003c/p>\n\u003cp>“What it’s going to be learning is race, gender, and class bias,” she said. “We’re baking those social stratifications in, and we’re making the biases even less apparent and even less easy for people to recognize.”\u003c/p>\n\u003cp>Sometimes, the recommendations Watson gives diverge sharply from what doctors would say for reasons that have nothing to do with science, such as medical insurance. In a poster presented at the Global Breast Cancer Conference 2017 in South Korea, researchers reported that the treatment Watson most often recommended for breast cancer patients simply wasn’t covered by the national insurance system.\u003c/p>\n\u003cp>IBM said it has convened an international group of advisers to gather input on Watson’s performance. It also said that the system can be customized to reflect variations in treatment practices, differences in drug availability and financial considerations, and that the company recently introduced tools reduce the time and cost of adapting Watson.\u003c/p>\n\u003cp>In a response to STAT’s questions, Memorial Sloan Kettering said international journals are part of the literature it provides to Watson, including the Lancet, the European Journal of Cancer, Annals of Oncology, and the BMJ. “As we do in all areas of cancer research, we will continue to observe and study how Watson for Oncology impacts care internationally, follow the evidence, and work with IBM to optimize the system,” the hospital said.\u003c/p>\n\u003cp>Some hospitals abroad are customizing the system for their patients, adding information about local treatments. Nan Chen, who manages the Watson for Oncology program at Bumrungrad International Hospital in Thailand, said his oncologists use Japanese guidelines, not American guidelines, for treating gastric cancer.\u003c/p>\n\u003cp>But he said doctors can find this localization redundant or unnecessary: They are not that interested in being told the same guidance they just taught Watson.\u003c/p>\n\u003cp>“Our doctors say, this treatment is our own treatment, we know that,” Chen said. “You don’t need to turn around and put those treatments in Watson, and let Watson tell us what kind of treatment that we are using here in the hospital.”\u003c/p>\n\u003cp>Chen said this modified system is incredibly beneficial, however — to a hospital in the capital of Mongolia that employs zero oncology specialists.\u003c/p>\n\u003cp>At UB Songdo Hospital, of which Chen’s company is a majority owner, doctors are following Watson’s suggestions nearly 100 percent of the time. Patients who otherwise would have been treated by generalists with little, if any, cancer training are now benefiting from top-level expertise.\u003c/p>\n\u003cp>“That is the kind of thing that IBM is dreaming about,” Chen said.\u003c/p>\n\u003cp>In South Korea, Dr. Taewoo Kang, a surgical oncologist at Pusan National University Hospital who specializes in breast cancer, pointed to another important problem that Watson needs to solve. Right now, it provides supporting evidence for the recommendations it makes, but doesn’t actually explain how it came to recommend that particular treatment for that particular patient.\u003c/p>\n\u003cp>Kang said that, sometimes, he will ask Watson for advice on a patient whose cancer has not spread to the lymph nodes, and Watson will recommend a type of chemotherapy drug called a taxane. But, he said, that therapy is normally used only if the cancer has spread to the lymph nodes. And, to support the recommendation, Watson will show a study demonstrating the effectiveness of the taxane for patients whose cancer did spread to their lymph nodes.\u003c/p>\n\u003cp>Kang is left confused as to why Watson recommended a drug that he does not normally use for patients like the one in front of him. And Watson can’t tell him why.\u003c/p>\n\u003cp>For all the concerns, some doctors around the world who use Watson insist that artificial intelligence will one day revolutionize health care. They say that clinicians are realizing concrete benefits — saving doctors valuable time searching for studies, better educating patients, and undercutting hierarchies in the clinic that might interfere with evidence-based treatment.\u003c/p>\n\u003cp>In Taiwan, Chiou said Watson immediately provides the “best data” from the literature about a treatment — survival rates, for example — relieving doctors of the task of searching the literature to compare each possible treatment.\u003c/p>\n\u003cp>Watson’s information also empowers patients, said Lee, the doctor who runs the Watson program at \u003ca href=\"http://www.koreatimes.co.kr/www/news/tech/2017/02/129_216534.html\" target=\"_blank\" rel=\"noopener noreferrer\">Gil Medical Center\u003c/a> in South Korea. Previously, doctors verbally explained different treatment options to patients. Now, physicians can give patients a comprehensive packet prepared by Watson, which includes potential treatment plans along with relevant scientific articles. Patients can do their own research about these treatments, and maybe even disagree with the doctor about the right course of action.\u003c/p>\n\u003cp>“This is one of the most important and significant changes,” Lee said.\u003c/p>\n\u003cp>Watson also holds senior doctors accountable to the data. At Gil Medical Center, patients sit in a room with five doctors and Watson itself, the interface displayed on a flat-screen television in the so-called “Watson center.” Lee said that Watson’s presence has a huge influence on the doctors’ decision-making process, leveling the hierarchy that traditionally prioritized the opinion of the senior doctor over junior colleagues.\u003c/p>\n\u003cp>[contextly_sidebar id=\"N3UBaH9vbepyeyhmYce1xcyzU851QDHX\"]Watson gives the junior physicians quick and easy access to data that might prove their elders wrong, displaying on the screen information such as the survival rate right alongside a recommended treatment. It would be humiliating for senior doctors to continue to push for a different treatment in light of this evidence, Lee said.\u003c/p>\n\u003cp>At Manipal Hospitals in India, Dr. S.P. Somashekhar said that while there are some regional disparities in Watson’s recommendations for patients with rectal and breast cancer, those cases are outliers: For the vast majority of patients, the program matched the recommendations given to patients by the hospital’s tumor board — a group of 20 physicians that typically study their cases for a week and spend an hour discussing them.\u003c/p>\n\u003cp>That means that in a handful of seconds, Watson did what it takes 20 doctors over a week to accomplish. “That is so precious and very highly valuable,” Somashekhar said. “Our physicians cannot discuss every case. For every case we discuss in the tumor board, there are five cases which we cannot discuss.”\u003c/p>\n\u003cp>While those benefits are significant, they fall short of breakthrough discoveries that could predict or eradicate disease.\u003c/p>\n\u003cp>IBM executives said that doesn’t mean Watson can’t accomplish those feats. Norden, the former deputy health officer for Watson for Oncology and Genomics, said the goal is to ultimately bring together streams of clinical trial data and real-world patient data, so that Watson could begin to pinpoint the best treatments on its own.\u003c/p>\n\u003cp>“My own belief is that over time we will be better at measuring and reporting outcomes, and that data will be increasingly influential,” he said. “Where cancer care is today, I don’t think that any computing system is ready to be let out into the world without a measure of expert human oversight.”\u003c/p>\n\u003cp>The bigger question for IBM is not whether health care will see a revolution in artificial intelligence but who will drive it.\u003c/p>\n\u003cp>One former IBM employee says the company could become a victim of its own marketing success — the unrealistic expectations it set are obscuring real accomplishments.\u003c/p>\n\u003cp>“IBM ought to quit trying to cure cancer,” said Peter Greulich, a former IBM brand manager who has written several books about IBM’s history and modern challenges. “They turned the marketing engine loose without controlling how to build and construct a product.”\u003c/p>\n\u003caside class=\"pullquote alignright\">'All they want to hear is that Watson is the answer. And it always has the right answer, and you get it right away, and it will be cheaper. But like anything else, it’s kind of human.'\u003ccite>Dr. Mark Kris, Memorial Sloan Kettering’s lead Watson trainer\u003c/cite>\u003c/aside>\n\u003cp>Greulich said IBM needs to invest more money in Watson and hire more people to make it successful. In the 1960s, he said, IBM spent about 11.5 times its annual earnings to develop its mainframe computer, a line of business that still accounts for much of its profitability today.\u003c/p>\n\u003cp>If it were to make an equivalent investment in Watson, it would need to spend $137 billion. “The only thing it’s spent that much money on is stock buybacks,” Greulich said.\u003c/p>\n\u003cp>IBM said it created the market for artificial intelligence and is pleased with the pace of Watson’s growth, noting that it and other new business units grew by more than $20 billion in the past three years. “It took Facebook and Amazon more than 13 years to grow $20 billion,” the company said in a statement.\u003c/p>\n\u003cp>Since Watson’s “Jeopardy!” demonstration in 2011, hundreds of companies have begun developing health care products using artificial intelligence. These include countless startups, but IBM also faces stiff competition from industry titans such as Amazon, Microsoft, Google, and the Optum division of UnitedHealth Group.\u003c/p>\n\u003cp>Google’s DeepMind, for example, recently displayed its own game-playing prowess, using its AlphaGo program to defeat a world champion in Go, a 3,000-year-old Chinese board game.\u003c/p>\n\u003cp>DeepMind is working with hospitals in London, where it is learning to detect eye disease and speed up the process of targeting treatments for head and neck cancers, although it has run into \u003ca href=\"http://www.wired.co.uk/article/ai-healthcare-gp-deepmind-privacy-problems\" target=\"_blank\" rel=\"noopener noreferrer\">privacy concerns\u003c/a>.\u003c/p>\n\u003cp>Meanwhile, Amazon has launched a health care lab, where it is exploring opportunities to mine data from electronic health records and potentially build a virtual doctor’s assistant.\u003c/p>\n\u003cp>A recent \u003ca href=\"https://javatar.bluematrix.com/pdf/fO5xcWjc\" target=\"_blank\" rel=\"noopener noreferrer\">report \u003c/a>by the financial firm Jefferies said IBM is quickly losing ground to competitors. “IBM appears outgunned in the war for AI talent and will likely see increasing competition,” the firm concluded.\u003c/p>\n\u003cp>While not specific to Watson’s health care products, the report said potential clients are backing away from the system because of significant consulting costs associated with its implementation. It also noted that Amazon has 10 times the job listings of IBM, which recently didn’t renew a small number of contractors that worked for the company following its acquisition of Truven, a company it bought for $2.6 billion last year to gain access to 100 million patient records.\u003c/p>\n\u003cp>In its statement, IBM said that the workers’ contracts ended and that it is continuing to hire aggressively in the Cambridge, Mass.-based Watson Health and other units, with more than 5,000 positions open in the U.S.\u003c/p>\n\u003cp>But the outlook for Watson for Oncology is challenging, say those who have worked closest with it. Kris, the lead trainer at Memorial Sloan Kettering, said the system has the potential to improve care and ensure more patients get expert treatment. But like a medical student, Watson is just learning to perform in the real world.\u003c/p>\n\u003cp>“Nobody wants to hear this,” Kris said. “All they want to hear is that Watson is the answer. And it always has the right answer, and you get it right away, and it will be cheaper. But like anything else, it’s kind of human.”\u003c/p>\n\u003cp>[ad floatright]\u003c/p>\n\u003cp>\u003ci>\u003cspan style=\"font-weight: 400\">This \u003ca href=\"https://www.statnews.com/2017/09/05/watson-ibm-cancer/\" target=\"_blank\" rel=\"noopener noreferrer\">story \u003c/a>was originally published by STAT, an online publication of Boston Globe Media that covers health, medicine, and scientific discovery.\u003c/span>\u003c/i>\u003c/p>\n\n","blocks":[],"excerpt":"Three years after IBM began selling 'Watson for Oncology' to recommend cancer treatments, it's falling short of the lofty expectations IBM created for it.","status":"publish","parent":0,"modified":1504727318,"stats":{"hasAudio":false,"hasVideo":true,"hasChartOrMap":false,"iframeSrcs":[],"hasGoogleForm":false,"hasGallery":false,"hasHearkenModule":false,"hasPolis":false,"paragraphCount":128,"wordCount":6511},"headData":{"title":"IBM Pitched Its Watson Supercomputer as a Revolution in Cancer Care. It’s Nowhere Close | KQED","description":"Three years after IBM began selling 'Watson for Oncology' to recommend cancer treatments, it's falling short of the lofty expectations IBM created for it.","ogTitle":"","ogDescription":"","ogImgId":"","twTitle":"","twDescription":"","twImgId":"","schema":{"@context":"http://schema.org","@type":"Article","headline":"IBM Pitched Its Watson Supercomputer as a Revolution in Cancer Care. It’s Nowhere Close","datePublished":"2017-09-06T18:33:16.000Z","dateModified":"2017-09-06T19:48:38.000Z","image":"https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"}},"disqusIdentifier":"435315 https://ww2.kqed.org/futureofyou/?p=435315","disqusUrl":"https://ww2.kqed.org/futureofyou/2017/09/06/ibm-pitched-its-watson-supercomputer-as-a-revolution-in-cancer-care-its-nowhere-close/","disqusTitle":"IBM Pitched Its Watson Supercomputer as a Revolution in Cancer Care. It’s Nowhere Close","nprByline":"Casey Ross and Ike Swetlitz\u003c/br>\u003ca href=\"https://www.statnews.com/2017/09/05/watson-ibm-cancer/\">STAT\u003c/a>","path":"/futureofyou/435315/ibm-pitched-its-watson-supercomputer-as-a-revolution-in-cancer-care-its-nowhere-close","audioTrackLength":null,"parsedContent":[{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003cp>It was an audacious undertaking, even for one of the most storied American companies: With a single machine, IBM would tackle humanity’s most vexing diseases and revolutionize medicine.\u003c/p>\n\u003cp>Breathlessly promoting its signature brand — Watson — IBM sought to capture the world’s imagination, and it quickly zeroed in on a high-profile target: cancer.\u003c/p>\n\u003cp>But three years after IBM began selling Watson to recommend the best cancer treatments to doctors around the world, a STAT investigation has found that the supercomputer isn’t living up to the lofty expectations IBM created for it. It is still struggling with the basic step of learning about different forms of cancer. Only a few dozen hospitals have adopted the system, which is a long way from IBM’s goal of establishing dominance in a multibillion-dollar market. And at foreign hospitals, physicians complained its advice is biased toward American patients and methods of care.\u003c/p>\n\u003caside class=\"pullquote alignright\">IBM has not exposed the product to critical review by outside scientists or conducted clinical trials to assess its effectiveness.\u003c/aside>\n\u003cp>STAT examined Watson for Oncology’s use, marketing, and performance in hospitals across the world, from South Korea to Slovakia to South Florida. Reporters interviewed dozens of doctors, IBM executives, artificial intelligence experts, and others familiar with the system’s underlying technology and rollout.\u003c/p>\n\u003cp>The interviews suggest that IBM, in its rush to bolster flagging revenue, unleashed a product without fully assessing the challenges of deploying it in hospitals globally. While it has \u003ca href=\"https://www.youtube.com/watch?v=au4kzyJUlrA\" target=\"_blank\" rel=\"noopener noreferrer\">emphatically marketed\u003c/a> Watson for cancer care, IBM hasn’t published any scientific papers demonstrating how the technology affects physicians and patients. As a result, its flaws are getting exposed on the front lines of care by doctors and researchers who say that the system, while promising in some respects, remains undeveloped.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"fullwidth"},"numeric":["fullwidth"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>“Watson for Oncology is in their toddler stage, and we have to wait and actively engage, hopefully to help them grow healthy,” said Dr. Taewoo Kang, a South Korean cancer specialist who has used the product.\u003c/p>\n\u003cp>At its heart, Watson for Oncology uses the cloud-based supercomputer to digest massive amounts of data — from doctor’s notes to medical studies to clinical guidelines. But its treatment recommendations are not based on its own insights from these data. Instead, they are based exclusively on training by human overseers, who laboriously feed Watson information about how patients with specific characteristics should be treated.\u003c/p>\n\u003cp>IBM executives acknowledged \u003ca href=\"https://www.ibm.com/watson/health/oncology-and-genomics/oncology/\" target=\"_blank\" rel=\"noopener noreferrer\">Watson for Oncology\u003c/a>, which has been in development for nearly six years, is in its infancy. But they said it is improving rapidly, noting that by year’s end, the system will offer guidance about treatment for 12 cancers that account for 80 percent of the world’s cases. They said it’s saving doctors time and ensuring that patients get top-quality care.\u003c/p>\n\u003cp>“We’re seeing stories come in where patients are saying, ‘It gave me peace of mind,’” Watson Health general manager Deborah DiSanzo said. “That makes us feel extraordinarily good that what we’re doing is going to make a difference for patients and their physicians.”\u003c/p>\n\u003cp>But contrary to IBM’s depiction of Watson as a digital prodigy, the supercomputer’s abilities are limited.\u003c/p>\n\u003cp>Perhaps the most stunning overreach is in the company’s claim that Watson for Oncology, through artificial intelligence, can sift through reams of data to generate new insights and identify, as an IBM sales rep put it, “even new approaches” to cancer care. STAT found that the system doesn’t create new knowledge and is artificially intelligent only in the most rudimentary sense of the term.\u003c/p>\n\u003cp>While Watson became a household name by winning the TV game show “Jeopardy!”, its programming is akin to a different game-playing machine: the Mechanical Turk, a chess-playing robot of the 1700s, which dazzled audiences but hid a secret — a human operator shielded inside.\u003c/p>\n\u003cp>In the case of Watson for Oncology, those human operators are a couple dozen physicians at a single, though highly respected, U.S. hospital: Memorial Sloan Kettering Cancer Center in New York. Doctors there are empowered to input their own recommendations into Watson, even when the evidence supporting those recommendations is thin.\u003c/p>\n\u003cp>The actual capabilities of Watson for Oncology are not well-understood by the public, and even by some of the hospitals that use it. It’s taken nearly six years of painstaking work by data engineers and doctors to train Watson in just seven types of cancer, and keep the system updated with the latest knowledge.\u003c/p>\n\u003cp>“It’s been a struggle to update, I’ll be honest,” said Dr. Mark Kris, Memorial Sloan Kettering’s lead Watson trainer. He noted that treatment guidelines for every metastatic lung cancer patient worldwide recently changed in the course of one week after a research presentation at a cancer conference. “Changing the system of cognitive computing doesn’t turn around on a dime like that,” he said. “You have to put in the literature, you have to put in cases.”\u003c/p>\n\u003cp>Watson grew out of an effort to transform IBM from an old-guard hardware company to one that operates in the cloud and along the cutting edge of artificial intelligence. Despite its use in an array of industries — from banking to manufacturing — it has failed to end a streak of 21 consecutive quarters of declining revenue at IBM. In the most recent quarter, revenue even slid from the same period last year in IBM’s cognitive solutions division — which is built around Watson and is supposed to be the future of its business.\u003c/p>\n\u003cp>\u003c/p>\u003cp>\u003c/p>\u003cp>In response to STAT’s questions, IBM said Watson, in health care and otherwise, remains on an upward trajectory and “is already an important part” of its $20 billion analytics business. Health care is a crucial part of the Watson enterprise. IBM employs 7,000 people in its Watson health division and sees the industry as a \u003ca href=\"https://www.ibm.com/investor/att/pdf/2017_Investor_Briefing_Financial_Discussion_charts.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">$200 billion market\u003c/a> over the next several years. Only financial services, at $300 billion, is considered a bigger opportunity by the company.\u003c/p>\n\u003cp>At stake in the supercomputer’s performance is not just the fortunes of a famed global company. In the world of medicine, Watson is also something of a digital canary — the most visible attempt to use artificial intelligence to identify the best ways to prevent and treat disease. The system’s larger goal, IBM executives say, is to democratize medical knowledge so that every patient, no matter the person’s geography or income level, will be able to access the best care.\u003c/p>\n\u003cp>But in cancer treatment, the pursuit of that utopian ideal has faltered.\u003c/p>\n\u003cp>STAT’s investigation focused on Watson for Oncology because that product is the furthest along in clinical care, though Watson sells separate packages to analyze genomic information and match patients to clinical trials. It’s also applying Watson to other tasks, including honing \u003ca href=\"https://www.ibm.com/watson/health/value-based-care/population-health-management/\" target=\"_blank\" rel=\"noopener noreferrer\">preventive medicine practices\u003c/a> and reading \u003ca href=\"https://www.ibm.com/watson/health/imaging/\" target=\"_blank\" rel=\"noopener noreferrer\">medical images\u003c/a>.\u003c/p>\n\u003cp>Doctors’ reliance on Watson for Oncology varies among hospitals. While institutions with fewer specialists lean more heavily on its recommendations, others relegate the system to a background role, like a paralegal whose main skill is researching existing knowledge.\u003c/p>\n\u003cp>\u003c/p>\u003cp>\u003c/p>\u003cp>Hospitals pay a per-patient fee for Watson for Oncology and other products enabled by the supercomputer. The amount depends on the number of products a hospital buys, and ranges between $200 and $1,000 per patient, according to DiSanzo. The system sometimes comes with consulting costs and is expensive to link with electronic medical records. At hospitals that don’t link it with their medical records, more time must be spent typing in patient information.\u003c/p>\n\u003cp>At Jupiter Medical Center in Florida, that task falls to nurse Jean Thompson, who spends about 90 minutes a week feeding data into the machine. Once she has completed that work, she clicks the “Ask Watson” button to get the supercomputer’s advice for treating patients.\u003c/p>\n\u003cp>On a recent morning, the results for a 73-year-old lung cancer patient were underwhelming: Watson recommended a chemotherapy regimen the oncologists had already flagged.\u003c/p>\n\u003cp>“It’s fine,” Dr. Sujal Shah, a medical oncologist, said of Watson’s treatment suggestion while discussing the case with colleagues.\u003c/p>\n\u003cp>He said later that the background information Watson provided, including medical journal articles, was helpful, giving him more confidence that using a specific chemotherapy was a sound idea. But the system did not directly help him make that decision, nor did it tell him anything he didn’t already know.\u003c/p>\n\u003cp>Jupiter is one of two U.S. hospitals that have adopted Watson for Oncology. The system has generated more business in India and Southeast Asia. Many doctors in those countries said Watson is saving time and helping more patients get quality care. But they also said its accuracy and overall value is limited by differing medical practices and economic circumstances.\u003c/p>\n\u003cp>Despite IBM’s marketing blitz, with years of high-profile Watson commercials featuring celebrities from Serena Williams to Bob Dylan to Jon Hamm, the company’s executives are not always gushing. In interviews with STAT, they acknowledged the system faces challenges and needs better integration with electronic medical records and more data on real patients to find patterns and suggest cutting-edge treatments.\u003c/p>\n\u003cp>“The goal as Watson gets smarter is for it to make some of those recommendations in a more automated way, to sort of suggest now may be the time and let us flip the switch” when a promising treatment option emerges, said Dr. Andrew Norden, a former IBM deputy health chief who left the company in early August. “As I describe it, you’re probably getting a sense it’s really hard and nuanced.”\u003c/p>\n\u003cp>Such nuance is absent from the careful narrative IBM has constructed to sell Watson.\u003c/p>\u003c/p>\u003cp>\u003cspan class='utils-parseShortcode-shortcodes-__youtubeShortcode__embedYoutube'>\n \u003cspan class='utils-parseShortcode-shortcodes-__youtubeShortcode__embedYoutubeInside'>\n \u003ciframe\n loading='lazy'\n class='utils-parseShortcode-shortcodes-__youtubeShortcode__youtubePlayer'\n type='text/html'\n src='//www.youtube.com/embed/UpFHNGF4F8o'\n title='//www.youtube.com/embed/UpFHNGF4F8o'\n allowfullscreen='true'\n style='border:0;'>\u003c/iframe>\n \u003c/span>\n \u003c/span>\u003c/p>\u003cp>\u003cp>It is by design that there is not one independent, third-party study that examines whether Watson for Oncology can deliver. IBM has not exposed the product to critical review by outside scientists or conducted clinical trials to assess its effectiveness.\u003c/p>\n\u003cp>While it’s not unheard of for companies to avoid external vetting early on, IBM’s circumstances are unusual because Watson for Oncology is not in development — it has already been deployed around the world.\u003c/p>\n\u003cp>Yoon Sup Choi, a South Korean venture capitalist and researcher who wrote a book about artificial intelligence in health care, said IBM isn’t required by regulatory agencies to do a clinical trial in South Korea or America before selling the system to hospitals. And given that hospitals are already using the system, a clinical trial would be unlikely to improve business prospects.\u003c/p>\n\u003cp>“It’s too risky, right?” Choi said. “If the result of the clinical trial is not very good — [if] there’s a marginal clinical benefit from Watson — it’s really bad news to the whole IBM.”\u003c/p>\n\u003cp>Pilar Ossorio, a professor of law and bioethics at University of Wisconsin Law School, said Watson should be subject to tighter regulation because of its role in treating patients. “As an ethical matter, and as a scientific matter, you should have to prove that there’s safety and efficacy before you can just go do this,” she said.\u003c/p>\n\u003caside class=\"pullquote alignright\">'Artificial intelligence will be adopted in all medical fields in the future. If that trend, that change is inevitable, then why don’t we just start early?'\u003ccite> Dr. Uhn Lee, Watson program, Gachon University Gil Medical Center, South Korea\u003c/cite>\u003c/aside>\n\u003cp>Norden dismissed the suggestion IBM should have been required to conduct a clinical trial before commercializing Watson, noting that many practices in medicine are widely accepted even though they aren’t supported by a randomized controlled trial.\u003c/p>\n\u003cp>“Has there ever been a randomized trial of parachutes for paratroopers?” Norden asked. “And the answer is, of course not, because there is a very strong intuitive value proposition. … So I believe that bringing the best information to bear on medical decision making is a no-brainer.”\u003c/p>\n\u003cp>IBM said in its statement that it has collaborated with the research community and presented data on Watson at industry gatherings and in peer-reviewed journals. Some doctors said they didn’t need to see more research to know that the system is valuable. “Artificial intelligence will be adopted in all medical fields in the future,” said Dr. Uhn Lee, who runs the Watson program at Gachon University Gil Medical Center in South Korea. “If that trend, that change is inevitable, then why don’t we just start early?”\u003c/p>\n\u003cp>So far, the only studies about Watson for Oncology are conference abstracts. The full results haven’t been published in peer-reviewed journals — and every study, save one, was either conducted by a paying customer or included IBM staff on the author list, or both. Most trumpet positive results, showing that Watson saves doctors time and has a high concordance rate with their treatment recommendations.\u003c/p>\n\u003cp>The “concordance” studies comprise the vast majority of the public research on Watson for Oncology. Doctors will ask Watson for its advice for treating a slew of patients, and then compare its recommendations to those of oncologists. In an unpublished study from Denmark, the rate of agreement was about 33 percent — so the hospital decided not to buy the system. In other countries, the rate can be as high as \u003ca href=\"http://meetinglibrary.asco.org/record/145389/abstract\" target=\"_blank\" rel=\"noopener noreferrer\">96 percent\u003c/a> for some cancers. But showing that Watson agrees with the doctors proves only that it is competent in applying existing methods of care, not that it can improve them.\u003c/p>\n\u003cp>IBM executives said they are pursuing studies to examine the impact on doctors and patients, although none has been completed to date.\u003c/p>\n\u003cp>Questions about Watson have begun spilling into public view, including in a \u003ca href=\"http://gizmodo.com/why-everyone-is-hating-on-watson-including-the-people-w-1797510888\" target=\"_blank\" rel=\"noopener noreferrer\">recent Gizmodo story\u003c/a> headlined “Why Everyone is Hating on IBM Watson — Including the People Who Helped Make It.” The most prominent failure occurred last February when MD Anderson Cancer Center, part of the University of Texas, cancelled its partnership with Watson.\u003c/p>\n\u003cp>The MD Anderson alliance was essentially the \u003ca href=\"http://www.washingtonpost.com/sf/national/2015/06/27/watsons-next-feat-taking-on-cancer/?utm_term=.ada22b3eefb7\" target=\"_blank\" rel=\"noopener noreferrer\">early face\u003c/a> of Watson in health care. The Houston hospital was among IBM’s first partners, and it was using the system to create its own expert oncology adviser, similar to the one IBM was developing with Memorial Sloan Kettering. But the project disintegrated amid internal allegations of overspending, delays, and mismanagement. In all, MD Anderson spent more than three years and $60 million — much of it on outside consultants — before shelving the effort.\u003c/p>\n\u003cp>The hospital declined to answer questions. But the project leader, Dr. Lynda Chin, in her first media interview on the subject, told STAT about the challenges she faced. Chin left MD Anderson before the project collapsed; a subsequent audit flagged several violations of procurement rules under her leadership.\u003c/p>\n\u003caside class=\"pullquote alignright\">How do we ensure the most important tenet in medicine: Do no harm?\u003c/aside>\n\u003cp>Chin said that Watson is a powerful technology, but that it is exceedingly difficult to make functional in health care. She and her team encountered numerous roadblocks, some of which still have not been fully addressed by IBM — at MD Anderson or elsewhere.\u003c/p>\n\u003cp>The cancer hospital’s first major challenge involved getting the machine to deal with the idiosyncrasies of medical records: the acronyms, human errors, shorthand phrases, and different styles of writing. “Teaching a machine to read a record is a lot harder than anyone thought,” she said. Her team spent countless hours on that problem, trying to get Watson to extract valuable information from medical records so that it could apply them to its recommendations.\u003c/p>\n\u003cp>Chin said her team also wrestled with deploying the system in clinical practice. Watson, even if guided by doctors, is as close as medicine has ever gotten to allowing a machine to help decide the treatments delivered to human beings. That carries with it thorny questions, such as how to test the safety of a digital treatment adviser, how to ensure its compliance with regulations, and how to incorporate it into the daily work of doctors and nurses.\u003c/p>\n\u003cp>“Importantly,” Chin said. “How do we create an environment that can ensure the most important tenet in medicine: Do no harm?”\u003c/p>\n\u003cp>Finally, the project ran into a bigger obstacle: Even if you can get Watson to understand patient variables and make competent treatment recommendations, how do you get it access to enough patient data, from enough different sources, to derive insights that could significantly advance the standard of care?\u003c/p>\n\u003cp>Chin said that was a showstopper. Watson did not have a connected network of institutions feeding data about specific cohorts of patients. “You may have 10,000 patients for lung cancer. That is still not a very big number when you think about it,” she said.\u003c/p>\n\u003cp>With data from many more patients, Chin said, you could see patterns — “subsets [of patients] that respond a certain way, subsets that don’t, subsets that have a certain toxicity. That pattern would help with better personalized and precision medicine. But we can’t get there without the ability to actually have a way of aggregating them.”\u003c/p>\n\u003cp>IBM told STAT that Chin’s work was separate from the effort to create Watson for Oncology, which was validated by cancer specialists at Memorial Sloan Kettering prior to its deployment. The company said that Watson for Oncology can extract and summarize substantial text from patient records, though the information must be verified by a clinician, and that it has made significant progress in obtaining more data to improve Watson’s performance. It pointed to partnerships with the health care publisher Elsevier and the analytics firm \u003ca href=\"https://www-03.ibm.com/press/us/en/pressrelease/47031.wss\" target=\"_blank\" rel=\"noopener noreferrer\">Doctor Evidence\u003c/a>.\u003c/p>\n\u003cp>To date, more than 50 hospitals on five continents have agreements with IBM, or intermediary technology companies, to use Watson for Oncology to treat patients, and others are using the genomics and clinical trials products.\u003c/p>\n\u003cp>But the partnership with Memorial Sloan Kettering, and the product that grew out of it, resulted in complications that IBM has papered over with carefully parsed statements and misleading marketing.\u003c/p>\n\u003cp>In its press releases, IBM celebrates \u003ca href=\"https://www.mskcc.org/about/innovative-collaborations/watson-oncology\" target=\"_blank\" rel=\"noopener noreferrer\">Memorial Sloan Kettering’s role\u003c/a> as the only trainer of Watson. After all, who better to educate the system than doctors at one of the world’s most renowned cancer hospitals?\u003c/p>\n\u003cp>But several doctors said Memorial Sloan Kettering’s training injects bias into the system, because the treatment recommendations it puts into Watson don’t always comport with the practices of doctors elsewhere in the world.\u003c/p>\n\u003cp>Given the same clinical scenario, doctors can — and often do — disagree about the best course of action, whether to recommend surgery or chemotherapy, or another treatment. Those discrepancies are especially wide for second- and third-line treatments given after an initial therapy fails, where evidence of benefits is slimmer and consensus more elusive.\u003c/p>\n\u003cp>Rather than acknowledge this dilemma, IBM executives, in marketing materials and interviews, have sought to downplay it. In an interview with STAT, DiSanzo, the head of Watson Health, rejected the idea that Memorial Sloan Kettering’s involvement creates any bias at all.\u003c/p>\n\u003cp>“The bias is taken out by the sheer amount of data we have,” she said, referring to patient cases and millions of articles and studies fed into Watson.\u003c/p>\n\u003cp>But that mischaracterizes how Watson for Oncology works. (IBM later claimed that DiSanzo was referring to Watson in general.)\u003c/p>\n\u003cp>The system is essentially Memorial Sloan Kettering in a portable box. Its treatment recommendations are based entirely on the training provided by doctors, who determine what information Watson needs to devise its guidance as well as what those recommendations should be.\u003c/p>\n\u003cp>When users ask Watson for advice, the system also searches published literature — some of which is curated by Memorial Sloan Kettering — to provide relevant studies and background information to support its recommendation. But the recommendation itself is derived from the training provided by the hospital’s doctors, not the outside literature.\u003c/p>\n\u003cp>Doctors at Memorial Sloan Kettering acknowledged their influence on Watson. “We are not at all hesitant about inserting our bias, because I think our bias is based on the next best thing to prospective randomized trials, which is having a vast amount of experience,” said Dr. Andrew Seidman, one of the hospital’s lead trainers of Watson. “So it’s a very unapologetic bias.”\u003c/p>\n\u003cp>Seidman said the hospital is careful to keep its training grounded in clinical evidence when the evidence exists, but it is not shy about giving its recommendations when it doesn’t. “We want cancer care to be democratized,” he said. “We don’t want doctors who don’t have the thousands and thousands of patients’ experience on a more rare cancer to be handicapped. We want to share that knowledge base.”\u003c/p>\n\u003cp>At a recent training session of Watson on Manhattan’s Upper East Side, the tensions involved in programming the system were on full display. STAT sat in as Memorial Sloan Kettering doctors, led by Seidman, gathered with IBM engineers to train Watson to treat bladder cancer. Five IBM engineers sat on one side of the table. Across from them were three oncologists — one specializing in surgery, another in radiation, and a third in chemotherapy and targeted medicines.\u003c/p>\n\u003cp>Several minutes into the discussion, the question arose of which treatment to recommend for patients whose cancers persisted through six rounds of chemotherapy. The options in such cases tend to be as slim as the evidence supporting them. Should Watson recommend a radical surgery to remove the bladder? Dr. Tim Donahue, the surgical oncologist, noted that such surgery seldom cures patients and is not associated with improved survival in his experience.\u003c/p>\n\u003cp>Then what about another course of chemotherapy combined with radiation?\u003c/p>\n\u003cp>When Watson gives its recommendations, it puts the top recommendation in green, alternative options in orange, and not recommended options in red.\u003c/p>\n\u003cp>But in some clinical scenarios, it’s difficult to tell the colors apart.\u003c/p>\n\u003cp>“This is the hard part of this whole game,” Dr. Marisa Kollmeier, the radiation oncologist, said during the training. “There’s a lack of evidence. And you don’t know if something should be in green without evidence. We don’t have a randomized trial to support every decision.”\u003c/p>\n\u003cp>But the task in front of them required the doctors to press ahead. And they did, rifling through an array of clinical scenarios. In some cases, a large body of evidence backed up their answers. But many others fell into a gray area or were clouded by the inevitable uncertainty of patient preferences.\u003c/p>\n\u003cp>The meeting was one of many in a months-long process to bring Watson up to speed in bladder cancer. Subsequent sessions would involve feeding it data on real patient cases at Memorial Sloan Kettering, so doctors could reinforce Watson’s training with repetition.\u003c/p>\n\u003cp>That training does not teach Watson to base its recommendations on the outcomes of these patients, whether they lived, or died or survived longer than similar patients. Rather, Watson makes its recommendations based on the treatment preferences of Memorial Sloan Kettering physicians.\u003c/p>\n\u003cp>At some institutions using Watson, IBM’s lack of clarity on the cancer center’s role causes confusion. Some seem to think they are getting advice from doctors around the world.\u003c/p>\n\u003cp>“As we tell the patients, it’s like another consultation, but it’s a worldwide consultation,” said Dr. K. Adam Lee, medical director of thoracic oncology at Jupiter Medical Center, when STAT visited in June.\u003c/p>\n\u003caside class=\"pullquote alignright\">Oncologists at one hospital said they have dropped the project altogether after finding that local doctors agreed with Watson in only about 33 percent of cases.\u003c/aside>\n\u003cp>“Really worldwide,” added Kerri Ward, an oncology nurse at the hospital. “It pulls from 300 journals, just for oncology, the clinical database, so the national clinical database, journals, textbooks, and then Sloan Kettering is the one that’s feeding in the clinical [information] currently.”\u003c/p>\n\u003cp>Robert Garrett, the CEO of Hackensack Meridian Health, a group in New Jersey that is using a version of Watson for Oncology, said the information in Watson is “global.”\u003c/p>\n\u003cp>“If you’re a patient that has colon cancer, they have in their database, as I understand it, how colon cancer is treated around the world, by different clinicians, what’s been the most effective treatment for different phases of colon cancer,” Garrett said. “That’s what IBM Watson brings to the table.”\u003c/p>\n\u003cp>None of that accurately depicts how Watson for Oncology works.\u003c/p>\n\u003cp>Several doctors who have examined Watson in other countries told STAT that Memorial Sloan Kettering’s role has given them pause. Researchers in Denmark and the Netherlands said hospitals in their countries have not signed on with Watson because it is too focused on the preferences of a few American doctors.\u003c/p>\n\u003cp>Martijn van Oijen, an epidemiologist and associate professor at Academic Medical Center in the Netherlands, said Memorial Sloan Kettering is packed with top specialists but doesn’t have a monopoly on cancer expertise. “The bad thing is, it’s a U.S.-based hospital with a different approach than some other hospitals in the world,” said van Oijen, who’s involved in a national initiative to evaluate technologies like Watson and is a strong believer in using artificial intelligence to help cancer doctors.\u003c/p>\n\u003cp>In Denmark, oncologists at one hospital said they have dropped the project altogether after finding that local doctors agreed with Watson in only about 33 percent of cases.\u003c/p>\n\u003cp>“We had a discussion with [IBM] that they had a very limited view on the international literature, basically, putting too much stress on American studies, and too little stress on big, international, European, and other-part-of-the-world studies,” said Dr. Leif Jensen, who directs the center at Rigshospitalet in Copenhagen that contains the oncology department.\u003c/p>\n\u003cp>In countries where doctors were trained in the United States, or they use similar treatment guidelines as the Memorial Sloan Kettering doctors, Watson for Oncology can be helpful. Taiwan uses the same guidelines as Americans, so Watson’s advice will be useful there, said Dr. Jeng-Fong Chiou, vice superintendent of the Taipei Cancer Center at Taipei Medical University, which started using Watson for Oncology with patients in July.\u003c/p>\n\u003cp>But he also said there are differences between American and Taiwanese patients — his patients often receive lower doses of drugs to minimize side effects — and that his oncologists will have to make adjustments from Watson’s recommendations.\u003c/p>\n\u003cp>The generally affluent population treated at Memorial Sloan Kettering doesn’t reflect the diversity of people around the world. The cases used to train Watson therefore don’t take into account the economic and social issues faced by patients in poorer countries, noted Ossorio, the University of Wisconsin law professor.\u003c/p>\n\u003cp>“What it’s going to be learning is race, gender, and class bias,” she said. “We’re baking those social stratifications in, and we’re making the biases even less apparent and even less easy for people to recognize.”\u003c/p>\n\u003cp>Sometimes, the recommendations Watson gives diverge sharply from what doctors would say for reasons that have nothing to do with science, such as medical insurance. In a poster presented at the Global Breast Cancer Conference 2017 in South Korea, researchers reported that the treatment Watson most often recommended for breast cancer patients simply wasn’t covered by the national insurance system.\u003c/p>\n\u003cp>IBM said it has convened an international group of advisers to gather input on Watson’s performance. It also said that the system can be customized to reflect variations in treatment practices, differences in drug availability and financial considerations, and that the company recently introduced tools reduce the time and cost of adapting Watson.\u003c/p>\n\u003cp>In a response to STAT’s questions, Memorial Sloan Kettering said international journals are part of the literature it provides to Watson, including the Lancet, the European Journal of Cancer, Annals of Oncology, and the BMJ. “As we do in all areas of cancer research, we will continue to observe and study how Watson for Oncology impacts care internationally, follow the evidence, and work with IBM to optimize the system,” the hospital said.\u003c/p>\n\u003cp>Some hospitals abroad are customizing the system for their patients, adding information about local treatments. Nan Chen, who manages the Watson for Oncology program at Bumrungrad International Hospital in Thailand, said his oncologists use Japanese guidelines, not American guidelines, for treating gastric cancer.\u003c/p>\n\u003cp>But he said doctors can find this localization redundant or unnecessary: They are not that interested in being told the same guidance they just taught Watson.\u003c/p>\n\u003cp>“Our doctors say, this treatment is our own treatment, we know that,” Chen said. “You don’t need to turn around and put those treatments in Watson, and let Watson tell us what kind of treatment that we are using here in the hospital.”\u003c/p>\n\u003cp>Chen said this modified system is incredibly beneficial, however — to a hospital in the capital of Mongolia that employs zero oncology specialists.\u003c/p>\n\u003cp>At UB Songdo Hospital, of which Chen’s company is a majority owner, doctors are following Watson’s suggestions nearly 100 percent of the time. Patients who otherwise would have been treated by generalists with little, if any, cancer training are now benefiting from top-level expertise.\u003c/p>\n\u003cp>“That is the kind of thing that IBM is dreaming about,” Chen said.\u003c/p>\n\u003cp>In South Korea, Dr. Taewoo Kang, a surgical oncologist at Pusan National University Hospital who specializes in breast cancer, pointed to another important problem that Watson needs to solve. Right now, it provides supporting evidence for the recommendations it makes, but doesn’t actually explain how it came to recommend that particular treatment for that particular patient.\u003c/p>\n\u003cp>Kang said that, sometimes, he will ask Watson for advice on a patient whose cancer has not spread to the lymph nodes, and Watson will recommend a type of chemotherapy drug called a taxane. But, he said, that therapy is normally used only if the cancer has spread to the lymph nodes. And, to support the recommendation, Watson will show a study demonstrating the effectiveness of the taxane for patients whose cancer did spread to their lymph nodes.\u003c/p>\n\u003cp>Kang is left confused as to why Watson recommended a drug that he does not normally use for patients like the one in front of him. And Watson can’t tell him why.\u003c/p>\n\u003cp>For all the concerns, some doctors around the world who use Watson insist that artificial intelligence will one day revolutionize health care. They say that clinicians are realizing concrete benefits — saving doctors valuable time searching for studies, better educating patients, and undercutting hierarchies in the clinic that might interfere with evidence-based treatment.\u003c/p>\n\u003cp>In Taiwan, Chiou said Watson immediately provides the “best data” from the literature about a treatment — survival rates, for example — relieving doctors of the task of searching the literature to compare each possible treatment.\u003c/p>\n\u003cp>Watson’s information also empowers patients, said Lee, the doctor who runs the Watson program at \u003ca href=\"http://www.koreatimes.co.kr/www/news/tech/2017/02/129_216534.html\" target=\"_blank\" rel=\"noopener noreferrer\">Gil Medical Center\u003c/a> in South Korea. Previously, doctors verbally explained different treatment options to patients. Now, physicians can give patients a comprehensive packet prepared by Watson, which includes potential treatment plans along with relevant scientific articles. Patients can do their own research about these treatments, and maybe even disagree with the doctor about the right course of action.\u003c/p>\n\u003cp>“This is one of the most important and significant changes,” Lee said.\u003c/p>\n\u003cp>Watson also holds senior doctors accountable to the data. At Gil Medical Center, patients sit in a room with five doctors and Watson itself, the interface displayed on a flat-screen television in the so-called “Watson center.” Lee said that Watson’s presence has a huge influence on the doctors’ decision-making process, leveling the hierarchy that traditionally prioritized the opinion of the senior doctor over junior colleagues.\u003c/p>\n\u003cp>\u003c/p>\u003cp>\u003c/p>\u003cp>Watson gives the junior physicians quick and easy access to data that might prove their elders wrong, displaying on the screen information such as the survival rate right alongside a recommended treatment. It would be humiliating for senior doctors to continue to push for a different treatment in light of this evidence, Lee said.\u003c/p>\n\u003cp>At Manipal Hospitals in India, Dr. S.P. Somashekhar said that while there are some regional disparities in Watson’s recommendations for patients with rectal and breast cancer, those cases are outliers: For the vast majority of patients, the program matched the recommendations given to patients by the hospital’s tumor board — a group of 20 physicians that typically study their cases for a week and spend an hour discussing them.\u003c/p>\n\u003cp>That means that in a handful of seconds, Watson did what it takes 20 doctors over a week to accomplish. “That is so precious and very highly valuable,” Somashekhar said. “Our physicians cannot discuss every case. For every case we discuss in the tumor board, there are five cases which we cannot discuss.”\u003c/p>\n\u003cp>While those benefits are significant, they fall short of breakthrough discoveries that could predict or eradicate disease.\u003c/p>\n\u003cp>IBM executives said that doesn’t mean Watson can’t accomplish those feats. Norden, the former deputy health officer for Watson for Oncology and Genomics, said the goal is to ultimately bring together streams of clinical trial data and real-world patient data, so that Watson could begin to pinpoint the best treatments on its own.\u003c/p>\n\u003cp>“My own belief is that over time we will be better at measuring and reporting outcomes, and that data will be increasingly influential,” he said. “Where cancer care is today, I don’t think that any computing system is ready to be let out into the world without a measure of expert human oversight.”\u003c/p>\n\u003cp>The bigger question for IBM is not whether health care will see a revolution in artificial intelligence but who will drive it.\u003c/p>\n\u003cp>One former IBM employee says the company could become a victim of its own marketing success — the unrealistic expectations it set are obscuring real accomplishments.\u003c/p>\n\u003cp>“IBM ought to quit trying to cure cancer,” said Peter Greulich, a former IBM brand manager who has written several books about IBM’s history and modern challenges. “They turned the marketing engine loose without controlling how to build and construct a product.”\u003c/p>\n\u003caside class=\"pullquote alignright\">'All they want to hear is that Watson is the answer. And it always has the right answer, and you get it right away, and it will be cheaper. But like anything else, it’s kind of human.'\u003ccite>Dr. Mark Kris, Memorial Sloan Kettering’s lead Watson trainer\u003c/cite>\u003c/aside>\n\u003cp>Greulich said IBM needs to invest more money in Watson and hire more people to make it successful. In the 1960s, he said, IBM spent about 11.5 times its annual earnings to develop its mainframe computer, a line of business that still accounts for much of its profitability today.\u003c/p>\n\u003cp>If it were to make an equivalent investment in Watson, it would need to spend $137 billion. “The only thing it’s spent that much money on is stock buybacks,” Greulich said.\u003c/p>\n\u003cp>IBM said it created the market for artificial intelligence and is pleased with the pace of Watson’s growth, noting that it and other new business units grew by more than $20 billion in the past three years. “It took Facebook and Amazon more than 13 years to grow $20 billion,” the company said in a statement.\u003c/p>\n\u003cp>Since Watson’s “Jeopardy!” demonstration in 2011, hundreds of companies have begun developing health care products using artificial intelligence. These include countless startups, but IBM also faces stiff competition from industry titans such as Amazon, Microsoft, Google, and the Optum division of UnitedHealth Group.\u003c/p>\n\u003cp>Google’s DeepMind, for example, recently displayed its own game-playing prowess, using its AlphaGo program to defeat a world champion in Go, a 3,000-year-old Chinese board game.\u003c/p>\n\u003cp>DeepMind is working with hospitals in London, where it is learning to detect eye disease and speed up the process of targeting treatments for head and neck cancers, although it has run into \u003ca href=\"http://www.wired.co.uk/article/ai-healthcare-gp-deepmind-privacy-problems\" target=\"_blank\" rel=\"noopener noreferrer\">privacy concerns\u003c/a>.\u003c/p>\n\u003cp>Meanwhile, Amazon has launched a health care lab, where it is exploring opportunities to mine data from electronic health records and potentially build a virtual doctor’s assistant.\u003c/p>\n\u003cp>A recent \u003ca href=\"https://javatar.bluematrix.com/pdf/fO5xcWjc\" target=\"_blank\" rel=\"noopener noreferrer\">report \u003c/a>by the financial firm Jefferies said IBM is quickly losing ground to competitors. “IBM appears outgunned in the war for AI talent and will likely see increasing competition,” the firm concluded.\u003c/p>\n\u003cp>While not specific to Watson’s health care products, the report said potential clients are backing away from the system because of significant consulting costs associated with its implementation. It also noted that Amazon has 10 times the job listings of IBM, which recently didn’t renew a small number of contractors that worked for the company following its acquisition of Truven, a company it bought for $2.6 billion last year to gain access to 100 million patient records.\u003c/p>\n\u003cp>In its statement, IBM said that the workers’ contracts ended and that it is continuing to hire aggressively in the Cambridge, Mass.-based Watson Health and other units, with more than 5,000 positions open in the U.S.\u003c/p>\n\u003cp>But the outlook for Watson for Oncology is challenging, say those who have worked closest with it. Kris, the lead trainer at Memorial Sloan Kettering, said the system has the potential to improve care and ensure more patients get expert treatment. But like a medical student, Watson is just learning to perform in the real world.\u003c/p>\n\u003cp>“Nobody wants to hear this,” Kris said. “All they want to hear is that Watson is the answer. And it always has the right answer, and you get it right away, and it will be cheaper. But like anything else, it’s kind of human.”\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"floatright"},"numeric":["floatright"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>\u003ci>\u003cspan style=\"font-weight: 400\">This \u003ca href=\"https://www.statnews.com/2017/09/05/watson-ibm-cancer/\" target=\"_blank\" rel=\"noopener noreferrer\">story \u003c/a>was originally published by STAT, an online publication of Boston Globe Media that covers health, medicine, and scientific discovery.\u003c/span>\u003c/i>\u003c/p>\n\n\u003c/div>\u003c/p>","attributes":{"named":{},"numeric":[]}}],"link":"/futureofyou/435315/ibm-pitched-its-watson-supercomputer-as-a-revolution-in-cancer-care-its-nowhere-close","authors":["byline_futureofyou_435315"],"categories":["futureofyou_1062"],"tags":["futureofyou_849","futureofyou_103","futureofyou_1275","futureofyou_177","futureofyou_1014"],"collections":["futureofyou_1097"],"featImg":"futureofyou_435325","label":"futureofyou_1097"},"futureofyou_274449":{"type":"posts","id":"futureofyou_274449","meta":{"index":"posts_1591205157","site":"futureofyou","id":"274449","score":null,"sort":[1478540165000]},"guestAuthors":[],"slug":"will-computers-ever-be-able-to-make-diagnoses-as-well-as-physicians","title":"Will Computers Ever Be as Good as Physicians at Diagnosing Patients?","publishDate":1478540165,"format":"image","headTitle":"KQED Future of You | KQED Science","labelTerm":{},"content":"\u003cp>\u003cem>This is an edited excerpt from Robert Wachter’s “\u003ca href=\"https://www.amazon.com/Digital-Doctor-Hope-Medicines-Computer/dp/0071849467\" target=\"_blank\">The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age\u003c/a>,” reprinted with permission from McGraw-Hill. Copyright 2015.\u003c/em>\u003c/p>\n\u003cp>Since 2012, Vinod Khosla, a co-founder of Sun Microsystems, has been predicting that most of what physicians currently do can, will, and should be done by computers. “By 2025,” he has written, “more data-driven, automated health care will displace up to 80 percent of physicians’ diagnostic and prescription work.”\u003c/p>\n\u003caside class=\"pullquote alignright\">A computer can’t read a patient’s tone of voice or anxious look. These clues—like one patient saying, “I have chest pain,” and another, “I HAVE CHEST PAIN!!!”—can make all the difference in diagnosis.\u003c/aside>\n\u003cp>Though Khosla’s comments have irked many a physician, I’m not willing to dismiss him as a kooky provocateur or a utopian techno-evangelist. First of all, his investment track record has made him a Silicon Valley rock star. More important, as recently as a decade ago, some very smart and savvy computer engineers and economists believed that another seemingly intractable problem, building a driverless car, was beyond the reach of modern technology. As of April 2014, the Google car had clocked nearly 700,000 miles and been involved in just two accidents.\u003c/p>\n\u003cp>If the driverless car weren’t enough of a challenge to human superiority, who could have watched IBM’s Watson supercomputer defeat the Jeopardy Hall of Famers in 2011 and not fretted about the future of physicians, or any highly skilled workers, for that matter?\u003c/p>\n\u003cp>\"Just as factory jobs were eliminated in the twentieth century by new assembly-line robots,” wrote all-time (human) Jeopardy champion Ken Jennings soon after the lopsided match ended, “Brad [Rutter, the other defeated champ] and I were the first knowledge-industry workers put out of work by the new generation of ‘thinking’ machines. ‘Quiz show contestant’ may be the first job made redundant by Watson, but I’m sure it won’t be the last.”\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>Soon after the well-publicized trouncing, IBM announced that one of its first “use cases” for Watson would be medicine. Sean Hogan, vice president for IBM Healthcare, told me that “health care jumped out as an area whose complexity and nuances would be\u003ca href=\"https://contextly.com/redirect/?id=K8AsBQdj7f:274449:4068:13:::sidebar:5820be532053f2-30720465\" target=\"_blank\"> receptive to what Watson was representing\u003c/a>.”\u003c/p>\n\u003cp>\u003cstrong>Sticking Up for Team Human\u003c/strong>\u003c/p>\n\u003cp>Andy McAfee, coauthor with Erik Brynjolfsson of the terrific book \"The Second Machine Age,\" agrees with Khosla that computers will ultimately take over much of what physicians do, including diagnosis. “I can’t see how that doesn’t happen,” McAfee, a self-described “technology optimist,” told me when we met for lunch near his MIT office. McAfee and Brynjolfsson argue that the confluence of staggering growth in computing power, zetabytes of fully networked information available on the Web, and the “combinatorial power” of innovation mean that areas that seemed like dead ends, such as artificial intelligence in medicine, are now within reach. They liken the speed with which old digital barriers are falling to Hemingway’s observation about how a person goes broke: “gradually, then suddenly.\"\u003c/p>\n\u003cp>In speaking with both McAfee and Khosla, I felt a strange obligation to stick up for my teams: humans and the subset of humans called doctors. I told McAfee that while I was in awe of the driverless car and IBM’s victories in chess (over world champion Garry Kasparov in 1997) and Jeopardy, he just didn’t understand how hard medicine is. Answering questions posed by Alex Trebek like, “While Maltese borrows many words from Italian, it developed from a dialect of this Semitic language” (the correct response is “What is Arabic?”—Watson answered it, and 65 of the 74 other questions it rang in for, correctly) is tricky, sure, but, at the end of the day, one is simply culling a series of databases to find a fact—a single right answer.\u003c/p>\n\u003caside class=\"pullquote alignright\">‘Quiz show contestant’ may be the first job made redundant by Watson, but I’m sure it won’t be the last.'\u003ccite>(Human) 'Jeopardy' champ Ken Jennings\u003c/cite>\u003c/aside>\n\u003cp>Medical diagnosis isn’t like that. For one thing, uncertainty is endemic, so that the “correct” answer is often a surprisingly probabilistic notion. For another, many diagnoses reveal themselves over time. The patient may present with, say, a headache, but not a worrisome one, and so the primary treatment is reassurance, Tylenol, and time. If the headache worsens over the next two weeks—particularly if it is now accompanied by additional symptoms such as weakness or nausea—that’s an entirely different story.\u003c/p>\n\u003cp>McAfee listened sympathetically—he’s obviously heard scores of versions of the \"You just don’t understand; my work is different\" argument—and then said, “I imagine there are a bunch of really smart geeks at IBM taking notes as guys like you describe this situation. In their heads, they’re asking, ‘How do I model that?’”\u003c/p>\n\u003cp>Undaunted, I tried another tack on Khosla when we met in his office in Menlo Park. “Vinod,” I said, “in medicine we have something we call the ‘eyeball test.’ That means I can see two patients whose numbers look the same”—things like temperature, heart rate, and blood counts—“and my training allows me to say, ‘That guy is sick [I pointed to an imaginary person across the imposing conference table] and the other is okay.’” And good doctors are usually right, I told him, as we possess a kind of sixth sense that we acquire from our training, our role models, and a thousand cases of trial and error.\u003c/p>\n\u003cp>Before Khosla could dismiss this as the usual whining from a dinosaur on the edge of extinction, I tossed him an example from his own world. “I’ll bet you have CEOs of start-ups constantly coming through this office pitching their companies,” I said. “I can imagine two companies that look the same on paper: both CEOs have Stanford MBAs; the proposals have similar financials. Your skill is to be able to point to one and say, ‘Winner’ and to the other, ‘Loser,’ and I’m guessing you’re right more often than not. You’re using information that isn’t measurable. Right?”\u003c/p>\n\u003cp>Nice try. He didn’t budge. “The question is, ‘Is it not measurable or is it not being measured?’” he responded. “And, when does your instinct work and when does it mislead? I think if you did a rigorous study, you’d find that your ‘eyeball test’ is far less effective than you think.”\u003c/p>\n\u003cp>https://www.youtube.com/watch?v=P18EdAKuC1U\u003c/p>\n\u003cp>\u003cstrong>Secrets of the Great Diagnosticians\u003c/strong>\u003c/p>\n\u003cp>There is a rich 50-year history of efforts to build artificial intelligence (AI) systems in health care, and it’s not a particularly uplifting story. Even technophiles admit that the quest to replace doctors with computers—or even the more modest ambition of providing them with useful guidance at the point of care—has been overhyped and unproductive. But times have changed. The growing prevalence of electronic health records offers grist for the AI and big data mills, grist that wasn’t available when the records were on paper. And in this, the Age of Watson, we have new techniques, like natural language processing and machine learning, at our disposal. Perhaps this is our “gradually, then suddenly” moment.\u003c/p>\n\u003caside class=\"pullquote alignright\">Early attempts to use computers for diagnosis were like tackling Saturday’s crossword puzzle in the New York Times before first mastering the one in USA Today.\u003c/aside>\n\u003cp>The public worships dynamic, innovative surgeons like Michael DeBakey; passionate, insightful researchers like Jonas Salk; and telegenic show horses like Mehmet Oz. But we seldom hear about those doctors whom other physicians tend to hold in the highest esteem: the great medical diagnosticians. These sages, like the legendary Johns Hopkins professors William Osler and A. McGehee Harvey, had the uncanny ability to deduce the truth from what others found to be a jumble of symptoms, signs, and lab results. In fact, Sir Arthur Conan Doyle, a physician by training, modeled Sherlock Holmes on one of his old professors, Joseph Bell, a renowned diagnostician at Edinburgh’s medical school.\u003c/p>\n\u003cp>For most doctors, diagnosis forms the essence of their practice (and of their professional souls), which may help explain why we find it so painful to believe that this particular skill could be replaced by silicon wafers.\u003c/p>\n\u003cp>In the 1970s, a Tufts kidney specialist named Jerome Kassirer (who later became editor of the New England Journal of Medicine) decided to try to unlock the cognitive secrets of the great diagnosticians. If he succeeded, the rewards could be great. The insights, problem-solving strategies, and reasoning patterns of these medical geniuses might be teachable to other physicians, perhaps even programmed into computers.\u003c/p>\n\u003cp>Kassirer focused first on the differential diagnosis, the method that doctors have long used to inventory and sort through their patients’ problems. The differential diagnosis is to a physician what the building of hypotheses is to a basic scientist: the core work of the professional mind. Let’s say a female patient complains of right lower abdominal pain and fever. We automatically begin to generate “a differential,” including appendicitis, pelvic inflammatory disease, kidney infection, and a host of less common disorders—some of them quite serious. Our job is to weigh the facts at hand in an effort to ultimately “rule in” one diagnosis on the list and “rule out” the others. Sometimes, the information we gather from the history and physical examination is sufficient.\u003c/p>\n\u003cp>More often, particularly when patients are truly ill, we require additional laboratory or radiographic studies to push one of the diagnoses over the “rule in” line. There is considerable skill, and no small amount of art, involved in this process. For one thing, we need to figure out whether the patient’s symptoms are part of a single disease or are manifestations of two or more distinct illnesses. The principle known as\u003ca href=\"http://www.medicinenet.com/script/main/art.asp?articlekey=26739\" target=\"_blank\"> Occam’s Razor\u003c/a> bids us to try to find a unifying diagnosis for all of a patient’s symptoms. But as soon as medical students memorize this so-called Law of Clinical Parsimony, we whipsaw them with \u003ca href=\"http://www.emergencymedicalparamedic.com/what-is-hickams-dictum/\" target=\"_blank\">Hickam’s Dictum\u003c/a>, which counters, irreverently, that “patients can have as many diseases as they damn well please.”\u003c/p>\n\u003cp>[contextly_sidebar id=\"fXsrKyRyGekfunOt3dsmZPdJAbzwHrbd\"]\u003c/p>\n\u003cp>Setting the “rule in” threshold is yet another challenge, since it’s wholly dependent on the context. For diseases with relatively benign treatments and prognoses—let’s say, stomach discomfort with no alarming features—I might make the diagnosis of “nonulcer dyspepsia” if I’m 75 percent certain that this is what’s going on. Why? Dyspepsia is a not-too-serious illness, the other illnesses that might present with the same symptoms aren’t likely to be acutely life-threatening either, and dyspepsia has a safe, inexpensive, and fairly effective treatment. All of this makes a 75 percent threshold high enough for me to try an acid-blocker and see what happens.\u003c/p>\n\u003cp>Now let’s turn to a patient who presents with acute shortness of breath and pleuritic chest pain. In this patient, I’m considering the diagnosis of pulmonary embolism (a blood clot to the lungs), a more serious disorder whose treatment (blood thinners) is riskier. Now, I’d want to be at least 95 percent sure before attaching that diagnostic label. And I won’t rule in a diagnosis of cancer—with its psychological freight, prognostic implications, and toxic treatments—unless I’m close to 100 percent certain, even if it takes a surgical biopsy to achieve this level of confidence.\u003c/p>\n\u003cp>Kassirer and his colleagues observed the diagnostic reasoning of scores of clinicians. They found that the good ones employed robust strategies to answer these knotty questions, even if they couldn’t always articulate what they were doing and why. The researchers ultimately came to appreciate that the physicians were engaging in a process called “iterative hypothesis testing” to transform the differential diagnosis (or, more accurately, diagnoses, since sick patients often have a variety of abnormalities to be explained) into something actionable. After hearing the initial portion of a case, the doctors began drawing possible scenarios to explain it, modifying their opinions as they went along and more information became available.\u003c/p>\n\u003cp>For example, when a physician confronts a case that begins with, “This 57-year-old man has three days of chest pain, shortness of breath, and lightheadedness,” she responds by thinking, “The worst thing this could be is a heart attack or a pulmonary embolism. I need to ask if the chest pain bores through to the back, which would make me worry about aortic dissection [a rip in the aorta]. I’ll also inquire about typical cardiac symptoms, such as sweating and nausea, and see if the pain is squeezing or radiates to the left arm or jaw. But even if it doesn’t, I’ll certainly get an EKG to rule out a heart attack or pericarditis [inflammation of the sac that surrounds the heart]. If he also reports a fever or a cough, I might begin to suspect pneumonia or pleurisy. The chest X-ray should help sort that out.”\u003c/p>\n\u003cp>Every answer the patient gives, and each positive or negative finding on the physical examination (yes, there is a heart murmur; no, the liver is not enlarged) triggers an automatic, almost intuitive recalibration of the most likely alternatives. When I see a master clinician at work—my favorite is my UCSF colleague Gurpreet Dhaliwal, who was profiled in a 2012\u003ca href=\"http://www.nytimes.com/2012/12/04/health/quest-to-eliminate-diagnostic-lapses.html\" target=\"_blank\"> New York Times article\u003c/a>—I know that these synapses are firing as he asks a patient a series of questions that may seem unrelated to the patient’s presenting complaint but are directed toward “narrowing the differential.” It turns out that there’s an even more impressive piece of cognitive magic going on. The master clinician embraces certain pieces of data (the patient’s trip to rural Thailand last year) while discarding others (an episode of belly pain and bloating three weeks ago). This is the part of diagnostic reasoning that beginners find most vexing, since they lack the foundational knowledge to understand why their teacher focused so intently on one nugget of information and all but ignored others that, to the novice, seemed equally crucial. How do the great diagnosticians make such choices?\u003c/p>\n\u003cp>We now recognize this as a relatively intuitive version of \u003ca href=\"http://www.medicinenet.com/script/main/art.asp?articlekey=10301\" target=\"_blank\">Bayes’ theorem\u003c/a>. Developed by the eighteenth-century British theologian-turned-mathematician Thomas Bayes, this theorem (often ignored by students because it is taught to them with the dryness of a Passover matzo) is the linchpin of clinical reasoning. In essence, Bayes’ theorem says that any medical test must be interpreted from two perspectives. The first: How accurate is the test—that is, how often does it give right or wrong answers? The second: How likely is it that this patient has the disease the test is looking for?\u003c/p>\n\u003cp>These deceptively simple questions explain why, in the early days of the AIDS epidemic (when HIV testing was far less accurate than it is today), it was silly to test heterosexual couples applying for a marriage license, since the vast majority of positive tests in this very low-risk group would be wrong. Similarly, they show why it is foolish to screen healthy 36-year-old executives with a cardiac treadmill test or a heart scan, since positive results will mostly be false positives, serving only to scare the bejesus out of the patients and run up bills for unnecessary follow-up tests. Conversely, in a 68-year-old smoker with diabetes and high cholesterol who develops squeezing chest pain while jogging, there is a 95 percent chance that those pains are from coronary artery disease. In this case, a negative treadmill test only lowers this probability to about 80 percent, so the clinician who reassures the patient that his negative test means that his heart is fine—“take some antacids; it’s OK to keep jogging”—is making a terrible, and potentially fatal, mistake.\u003c/p>\n\u003cp>\u003cstrong>The AI Challenge\u003c/strong>\u003c/p>\n\u003cp>As if this weren’t complicated enough for the poor IBM engineer gearing up to retool Watson from answering questions about “Potent Potables” to diagnosing sick patients, there’s more. While the EHR at least offers a fighting chance for computerized diagnosis (older medical AI programs, built in the pen-and-paper era, required busy physicians to write their notes and then reenter all the key data), parsing an electronic medical record is far from straightforward. Natural language processing is getting much better, but it still has real problems with negation (“the patient has no history of chest pain or cough”) and with family history (“there is a history of arthritis in the patient’s sister, but his mother is well”), to name just a couple of issues. Certain terms have multiple meanings: when written by a psychiatrist, the term depression is likely to refer to a mood disorder, while when it appears in a cardiologist’s note (“there was no evidence of ST-depression”) it probably refers to a dip in the EKG tracing that is often a clue to coronary disease. Ditto abbreviations: Does the patient with “MS” have multiple sclerosis or mitral stenosis, a sticky heart valve? Finally, the computer can’t read a patient’s tone of voice or the anxious look on her face, although engineers are working on this. These clues—like one patient saying, “I have chest pain,” and another, “I HAVE CHEST PAIN!!!”—can make all the difference in the world diagnostically.\u003c/p>\n\u003cp>Perhaps the trickiest problem of all is that—at least today—the very collection of the facts needed to feed an AI system is itself a cognitively complex process. Let’s return to the example of aortic dissection, a rip in the aorta that is often fatal if it is not treated promptly. If the initial history raises the slightest concern about dissection, I’m going to ask questions about whether the pain bores through to the back and check carefully for the quiet murmur of aortic insufficiency as well as for asymmetric blood pressure readings in the two arms, all clues to dissection. If I don’t harbor a suspicion of this scary (and unusual) disease, I’m not going to look for these things—they’re not part of a routine exam.\u003c/p>\n\u003cp>Decades ago, MIT’s Peter Szolovits, an AI expert who worked with Kassirer and his colleagues in the early days, gave up thinking about diagnosis as a simple matter of question answering. This was mostly because he came to appreciate the importance of timing—a nonissue in Jeopardy but a pivotal one in medicine. “A heart attack that happened five years ago has different implications from one that happened five minutes ago,” he explained, and a computer can’t “know” this unless it is programmed to do so. (It turns out that such issues of foundational knowledge are fundamental in AI—computers have no way of “knowing” some of the basic assumptions that allow us to get through our days, things like water is wet, love is good, and death is permanent.)\u003c/p>\n\u003cp>Moreover, much of medical reasoning relies on feedback loops: observing how events unfold and using that information to refine the diagnostic possibilities.We think a patient has bacterial pneumonia, and so we treat the “pneumonia” with antibiotics, but the patient’s fever doesn’t break after three days. So now we consider the possibility of tuberculosis or lupus. This is the cognitive work of the practicing clinician—focused a bit less on “What is the diagnosis?” and more on “How do I best manage this situation?”—and an AI program that doesn’t account for this will be of limited value.\u003c/p>\n\u003cp>\u003cstrong>Early Attempts\u003c/strong>\u003c/p>\n\u003cp>Now that you appreciate the nature of the problem, it’s easy (in retrospect, at least) to see why the choice by early health care computer experts to focus on diagnosis was risky, perhaps even wrongheaded. It’s like tackling Saturday’s crossword puzzle in the New York Times before first mastering the one in USA Today.\u003c/p>\n\u003cp>Larry Fagan, an early Stanford computing pioneer, told me, “We were not naive about the complexity. It’s just that it was the most exciting question.” Diagnosis is not just exciting, it’s at the heart of safe medical care. Diagnostic errors are common, and they can be fatal. A number of autopsy studies conducted over the past 40 years have shown that major diagnoses were overlooked in nearly one in five patients. With the advent of CT scans and MRIs, the number has gone down a bit, but it still hovers around one in ten. Diagnostic errors contribute to 40,000 to 80,000 deaths per year in the United States. And reviews of malpractice cases have demonstrated that diagnostic errors are the most common source of mistakes leading to successful lawsuits.\u003c/p>\n\u003cp>Medical IT experts jumped into the fray in the 1970s, designing a series of computer programs that they believed could help physicians be better diagnosticians, or perhaps even replace them entirely. That decade’s literature was replete with enthusiastic articles about how microprocessors, programmed to think like experts, would soon replace the brains of harried doctors. The attitude was captured by one early computing pioneer in a 1971 paean to his computer: “It is immune from fatigue and carelessness; and it works day and night, weekends and holidays, without coffee breaks, overtime, fringe benefits or human courtesy.”\u003c/p>\n\u003cp>By the mid-1980s, disappointment had set in. The tools that had seemed so promising a decade earlier were, by and large, unable to manage the complexity of clinical medicine, and they garnered few clinician advocates and miniscule commercial adoption. The medical AI movement skidded to a halt, marking the start of a 20-year period that insiders still refer to as the “AI winter.” Ted Shortliffe, one of the field’s longstanding leaders, has said that the early experience with programs like INTERNIST, DXplain, and MYCIN reminded him of this cartoon:\u003c/p>\n\u003cp>\u003ca href=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2016/11/cartoon.jpg\">\u003cimg class=\"aligncenter size-full wp-image-276235\" src=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2016/11/cartoon.jpg\" alt=\"cartoon\" width=\"300\" height=\"373\" srcset=\"https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon.jpg 300w, https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon-160x199.jpg 160w, https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon-240x298.jpg 240w\" sizes=\"(max-width: 300px) 100vw, 300px\">\u003c/a>\u003c/p>\n\u003cp>\u003cstrong>'Version 0'\u003c/strong>\u003c/p>\n\u003cp>Vinod Khosla is prepared for this. He knows that even today’s generation of medical AI programs will produce some crazy output, akin to when Watson famously mistook Toronto for an American city during its Jeopardy triumph. (It was worse in rehearsal, when Watson referred to civil rights leader Malcolm X as “Malcolm Ten.”) Khosla points out that the enormous cellphones of the late 1980s would seem equally ridiculous when placed alongside our iPhone 6.0s. He calls today’s medical AI programs “Version 0,” and cautions that people should “expect these early systems and tools to be the butt of jokes from many a writer and physician.”\u003c/p>\n\u003cp>These cases illustrate a perennial debate in AI, one that pits two camps against each other: the “neats” and the “scruffies.” The neats seek solutions that are elegant and provable; they try to model the way experts think and work, and then code that into AI tools. The scruffies are the pragmatists, the hackers, the crazy ones; they believe that problems should be attacked through whatever means work, and that modeling the behavior of experts or the scientific truth of a situation isn’t all that important. IBM’s breakthrough was to figure out that a combination of neat and scruffy—programming in some of the core rules of the game, but then folding in the fruits of machine learning and natural language processing—could solve truly complicated problems.\u003c/p>\n\u003cp>When he was asked about the difference between human thinking and Watson’s method, Eric Brown, who runs IBM’s Watson Technologies group, gave a careful answer (note the shout-out to the humans, the bit players who made it all possible):\u003c/p>\n\u003cblockquote>\u003cp>A lot of the way that Watson works is motivated by the way that humans analyze problems and go about trying to find solutions, especially when it comes to dealing with complex problems where there are a number of intermediate steps toget you to the final answer. So it certainly is inspired by that process. . . . But a lot of it is different from the ways humans work; it tends to leverage the powers and advantages of a computer system, and its ability to rapidly analyze huge amounts of data and text that humans just can’t keep track of.\u003c/p>\u003c/blockquote>\n\u003cp>However Watson works, we find ourselves today in a world with new tools, new mental models, and a new sense of optimism that computers can do pretty much anything. But have we finally reached the age when computers can master the art of clinical reasoning?\u003c/p>\n\u003cp>I asked Eric Brown, who worked on the \"Jeopardy\" project and is now helping to lead Watson’s efforts in medicine, what the equivalent event might be in health care, the moment when his team could finally congratulate itself on its successes. I wondered if it would be the creation of some kind of holographic physician—like “\u003ca href=\"http://memory-alpha.wikia.com/wiki/Emergency_Medical_Holographic_program\" target=\"_blank\">The Doctor\u003c/a>” on Star Trek Voyager—with Watson serving as the cognitive engine. His answer, though, reflected the deep respect he and his colleagues have for the magnitude of the challenge:\u003c/p>\n\u003cp>[ad floatright]\u003c/p>\n\u003cp>“It will be when we have a technology that physicians suddenly can’t live without.”\u003c/p>\n\n","blocks":[],"excerpt":"When it comes to the art of medical diagnosis, has Team Human finally triumphed over AI? Or is it only a matter of time before computers supplant the physician's brain? ","status":"publish","parent":0,"modified":1517000026,"stats":{"hasAudio":false,"hasVideo":true,"hasChartOrMap":false,"iframeSrcs":[],"hasGoogleForm":false,"hasGallery":false,"hasHearkenModule":false,"hasPolis":false,"paragraphCount":50,"wordCount":4460},"headData":{"title":"Will Computers Ever Be as Good as Physicians at Diagnosing Patients? | KQED","description":"When it comes to the art of medical diagnosis, has Team Human finally triumphed over AI? Or is it only a matter of time before computers supplant the physician's brain? ","ogTitle":"","ogDescription":"","ogImgId":"","twTitle":"","twDescription":"","twImgId":"","schema":{"@context":"http://schema.org","@type":"Article","headline":"Will Computers Ever Be as Good as Physicians at Diagnosing Patients?","datePublished":"2016-11-07T17:36:05.000Z","dateModified":"2018-01-26T20:53:46.000Z","image":"https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"}},"disqusIdentifier":"274449 http://ww2.kqed.org/futureofyou/?p=274449","disqusUrl":"https://ww2.kqed.org/futureofyou/2016/11/07/will-computers-ever-be-able-to-make-diagnoses-as-well-as-physicians/","disqusTitle":"Will Computers Ever Be as Good as Physicians at Diagnosing Patients?","source":"Future of You","customPermalink":"2016/11/07/AI-computers-diagnosis-watson/","nprByline":"Bob Wachter","path":"/futureofyou/274449/will-computers-ever-be-able-to-make-diagnoses-as-well-as-physicians","audioTrackLength":null,"parsedContent":[{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003cp>\u003cem>This is an edited excerpt from Robert Wachter’s “\u003ca href=\"https://www.amazon.com/Digital-Doctor-Hope-Medicines-Computer/dp/0071849467\" target=\"_blank\">The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age\u003c/a>,” reprinted with permission from McGraw-Hill. Copyright 2015.\u003c/em>\u003c/p>\n\u003cp>Since 2012, Vinod Khosla, a co-founder of Sun Microsystems, has been predicting that most of what physicians currently do can, will, and should be done by computers. “By 2025,” he has written, “more data-driven, automated health care will displace up to 80 percent of physicians’ diagnostic and prescription work.”\u003c/p>\n\u003caside class=\"pullquote alignright\">A computer can’t read a patient’s tone of voice or anxious look. These clues—like one patient saying, “I have chest pain,” and another, “I HAVE CHEST PAIN!!!”—can make all the difference in diagnosis.\u003c/aside>\n\u003cp>Though Khosla’s comments have irked many a physician, I’m not willing to dismiss him as a kooky provocateur or a utopian techno-evangelist. First of all, his investment track record has made him a Silicon Valley rock star. More important, as recently as a decade ago, some very smart and savvy computer engineers and economists believed that another seemingly intractable problem, building a driverless car, was beyond the reach of modern technology. As of April 2014, the Google car had clocked nearly 700,000 miles and been involved in just two accidents.\u003c/p>\n\u003cp>If the driverless car weren’t enough of a challenge to human superiority, who could have watched IBM’s Watson supercomputer defeat the Jeopardy Hall of Famers in 2011 and not fretted about the future of physicians, or any highly skilled workers, for that matter?\u003c/p>\n\u003cp>\"Just as factory jobs were eliminated in the twentieth century by new assembly-line robots,” wrote all-time (human) Jeopardy champion Ken Jennings soon after the lopsided match ended, “Brad [Rutter, the other defeated champ] and I were the first knowledge-industry workers put out of work by the new generation of ‘thinking’ machines. ‘Quiz show contestant’ may be the first job made redundant by Watson, but I’m sure it won’t be the last.”\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"fullwidth"},"numeric":["fullwidth"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>Soon after the well-publicized trouncing, IBM announced that one of its first “use cases” for Watson would be medicine. Sean Hogan, vice president for IBM Healthcare, told me that “health care jumped out as an area whose complexity and nuances would be\u003ca href=\"https://contextly.com/redirect/?id=K8AsBQdj7f:274449:4068:13:::sidebar:5820be532053f2-30720465\" target=\"_blank\"> receptive to what Watson was representing\u003c/a>.”\u003c/p>\n\u003cp>\u003cstrong>Sticking Up for Team Human\u003c/strong>\u003c/p>\n\u003cp>Andy McAfee, coauthor with Erik Brynjolfsson of the terrific book \"The Second Machine Age,\" agrees with Khosla that computers will ultimately take over much of what physicians do, including diagnosis. “I can’t see how that doesn’t happen,” McAfee, a self-described “technology optimist,” told me when we met for lunch near his MIT office. McAfee and Brynjolfsson argue that the confluence of staggering growth in computing power, zetabytes of fully networked information available on the Web, and the “combinatorial power” of innovation mean that areas that seemed like dead ends, such as artificial intelligence in medicine, are now within reach. They liken the speed with which old digital barriers are falling to Hemingway’s observation about how a person goes broke: “gradually, then suddenly.\"\u003c/p>\n\u003cp>In speaking with both McAfee and Khosla, I felt a strange obligation to stick up for my teams: humans and the subset of humans called doctors. I told McAfee that while I was in awe of the driverless car and IBM’s victories in chess (over world champion Garry Kasparov in 1997) and Jeopardy, he just didn’t understand how hard medicine is. Answering questions posed by Alex Trebek like, “While Maltese borrows many words from Italian, it developed from a dialect of this Semitic language” (the correct response is “What is Arabic?”—Watson answered it, and 65 of the 74 other questions it rang in for, correctly) is tricky, sure, but, at the end of the day, one is simply culling a series of databases to find a fact—a single right answer.\u003c/p>\n\u003caside class=\"pullquote alignright\">‘Quiz show contestant’ may be the first job made redundant by Watson, but I’m sure it won’t be the last.'\u003ccite>(Human) 'Jeopardy' champ Ken Jennings\u003c/cite>\u003c/aside>\n\u003cp>Medical diagnosis isn’t like that. For one thing, uncertainty is endemic, so that the “correct” answer is often a surprisingly probabilistic notion. For another, many diagnoses reveal themselves over time. The patient may present with, say, a headache, but not a worrisome one, and so the primary treatment is reassurance, Tylenol, and time. If the headache worsens over the next two weeks—particularly if it is now accompanied by additional symptoms such as weakness or nausea—that’s an entirely different story.\u003c/p>\n\u003cp>McAfee listened sympathetically—he’s obviously heard scores of versions of the \"You just don’t understand; my work is different\" argument—and then said, “I imagine there are a bunch of really smart geeks at IBM taking notes as guys like you describe this situation. In their heads, they’re asking, ‘How do I model that?’”\u003c/p>\n\u003cp>Undaunted, I tried another tack on Khosla when we met in his office in Menlo Park. “Vinod,” I said, “in medicine we have something we call the ‘eyeball test.’ That means I can see two patients whose numbers look the same”—things like temperature, heart rate, and blood counts—“and my training allows me to say, ‘That guy is sick [I pointed to an imaginary person across the imposing conference table] and the other is okay.’” And good doctors are usually right, I told him, as we possess a kind of sixth sense that we acquire from our training, our role models, and a thousand cases of trial and error.\u003c/p>\n\u003cp>Before Khosla could dismiss this as the usual whining from a dinosaur on the edge of extinction, I tossed him an example from his own world. “I’ll bet you have CEOs of start-ups constantly coming through this office pitching their companies,” I said. “I can imagine two companies that look the same on paper: both CEOs have Stanford MBAs; the proposals have similar financials. Your skill is to be able to point to one and say, ‘Winner’ and to the other, ‘Loser,’ and I’m guessing you’re right more often than not. You’re using information that isn’t measurable. Right?”\u003c/p>\n\u003cp>Nice try. He didn’t budge. “The question is, ‘Is it not measurable or is it not being measured?’” he responded. “And, when does your instinct work and when does it mislead? I think if you did a rigorous study, you’d find that your ‘eyeball test’ is far less effective than you think.”\u003c/p>\u003c/p>\u003cp>\u003cspan class='utils-parseShortcode-shortcodes-__youtubeShortcode__embedYoutube'>\n \u003cspan class='utils-parseShortcode-shortcodes-__youtubeShortcode__embedYoutubeInside'>\n \u003ciframe\n loading='lazy'\n class='utils-parseShortcode-shortcodes-__youtubeShortcode__youtubePlayer'\n type='text/html'\n src='//www.youtube.com/embed/P18EdAKuC1U'\n title='//www.youtube.com/embed/P18EdAKuC1U'\n allowfullscreen='true'\n style='border:0;'>\u003c/iframe>\n \u003c/span>\n \u003c/span>\u003c/p>\u003cp>\u003cp>\u003cstrong>Secrets of the Great Diagnosticians\u003c/strong>\u003c/p>\n\u003cp>There is a rich 50-year history of efforts to build artificial intelligence (AI) systems in health care, and it’s not a particularly uplifting story. Even technophiles admit that the quest to replace doctors with computers—or even the more modest ambition of providing them with useful guidance at the point of care—has been overhyped and unproductive. But times have changed. The growing prevalence of electronic health records offers grist for the AI and big data mills, grist that wasn’t available when the records were on paper. And in this, the Age of Watson, we have new techniques, like natural language processing and machine learning, at our disposal. Perhaps this is our “gradually, then suddenly” moment.\u003c/p>\n\u003caside class=\"pullquote alignright\">Early attempts to use computers for diagnosis were like tackling Saturday’s crossword puzzle in the New York Times before first mastering the one in USA Today.\u003c/aside>\n\u003cp>The public worships dynamic, innovative surgeons like Michael DeBakey; passionate, insightful researchers like Jonas Salk; and telegenic show horses like Mehmet Oz. But we seldom hear about those doctors whom other physicians tend to hold in the highest esteem: the great medical diagnosticians. These sages, like the legendary Johns Hopkins professors William Osler and A. McGehee Harvey, had the uncanny ability to deduce the truth from what others found to be a jumble of symptoms, signs, and lab results. In fact, Sir Arthur Conan Doyle, a physician by training, modeled Sherlock Holmes on one of his old professors, Joseph Bell, a renowned diagnostician at Edinburgh’s medical school.\u003c/p>\n\u003cp>For most doctors, diagnosis forms the essence of their practice (and of their professional souls), which may help explain why we find it so painful to believe that this particular skill could be replaced by silicon wafers.\u003c/p>\n\u003cp>In the 1970s, a Tufts kidney specialist named Jerome Kassirer (who later became editor of the New England Journal of Medicine) decided to try to unlock the cognitive secrets of the great diagnosticians. If he succeeded, the rewards could be great. The insights, problem-solving strategies, and reasoning patterns of these medical geniuses might be teachable to other physicians, perhaps even programmed into computers.\u003c/p>\n\u003cp>Kassirer focused first on the differential diagnosis, the method that doctors have long used to inventory and sort through their patients’ problems. The differential diagnosis is to a physician what the building of hypotheses is to a basic scientist: the core work of the professional mind. Let’s say a female patient complains of right lower abdominal pain and fever. We automatically begin to generate “a differential,” including appendicitis, pelvic inflammatory disease, kidney infection, and a host of less common disorders—some of them quite serious. Our job is to weigh the facts at hand in an effort to ultimately “rule in” one diagnosis on the list and “rule out” the others. Sometimes, the information we gather from the history and physical examination is sufficient.\u003c/p>\n\u003cp>More often, particularly when patients are truly ill, we require additional laboratory or radiographic studies to push one of the diagnoses over the “rule in” line. There is considerable skill, and no small amount of art, involved in this process. For one thing, we need to figure out whether the patient’s symptoms are part of a single disease or are manifestations of two or more distinct illnesses. The principle known as\u003ca href=\"http://www.medicinenet.com/script/main/art.asp?articlekey=26739\" target=\"_blank\"> Occam’s Razor\u003c/a> bids us to try to find a unifying diagnosis for all of a patient’s symptoms. But as soon as medical students memorize this so-called Law of Clinical Parsimony, we whipsaw them with \u003ca href=\"http://www.emergencymedicalparamedic.com/what-is-hickams-dictum/\" target=\"_blank\">Hickam’s Dictum\u003c/a>, which counters, irreverently, that “patients can have as many diseases as they damn well please.”\u003c/p>\n\u003cp>\u003c/p>\u003cp>\u003c/p>\u003cp>\u003c/p>\n\u003cp>Setting the “rule in” threshold is yet another challenge, since it’s wholly dependent on the context. For diseases with relatively benign treatments and prognoses—let’s say, stomach discomfort with no alarming features—I might make the diagnosis of “nonulcer dyspepsia” if I’m 75 percent certain that this is what’s going on. Why? Dyspepsia is a not-too-serious illness, the other illnesses that might present with the same symptoms aren’t likely to be acutely life-threatening either, and dyspepsia has a safe, inexpensive, and fairly effective treatment. All of this makes a 75 percent threshold high enough for me to try an acid-blocker and see what happens.\u003c/p>\n\u003cp>Now let’s turn to a patient who presents with acute shortness of breath and pleuritic chest pain. In this patient, I’m considering the diagnosis of pulmonary embolism (a blood clot to the lungs), a more serious disorder whose treatment (blood thinners) is riskier. Now, I’d want to be at least 95 percent sure before attaching that diagnostic label. And I won’t rule in a diagnosis of cancer—with its psychological freight, prognostic implications, and toxic treatments—unless I’m close to 100 percent certain, even if it takes a surgical biopsy to achieve this level of confidence.\u003c/p>\n\u003cp>Kassirer and his colleagues observed the diagnostic reasoning of scores of clinicians. They found that the good ones employed robust strategies to answer these knotty questions, even if they couldn’t always articulate what they were doing and why. The researchers ultimately came to appreciate that the physicians were engaging in a process called “iterative hypothesis testing” to transform the differential diagnosis (or, more accurately, diagnoses, since sick patients often have a variety of abnormalities to be explained) into something actionable. After hearing the initial portion of a case, the doctors began drawing possible scenarios to explain it, modifying their opinions as they went along and more information became available.\u003c/p>\n\u003cp>For example, when a physician confronts a case that begins with, “This 57-year-old man has three days of chest pain, shortness of breath, and lightheadedness,” she responds by thinking, “The worst thing this could be is a heart attack or a pulmonary embolism. I need to ask if the chest pain bores through to the back, which would make me worry about aortic dissection [a rip in the aorta]. I’ll also inquire about typical cardiac symptoms, such as sweating and nausea, and see if the pain is squeezing or radiates to the left arm or jaw. But even if it doesn’t, I’ll certainly get an EKG to rule out a heart attack or pericarditis [inflammation of the sac that surrounds the heart]. If he also reports a fever or a cough, I might begin to suspect pneumonia or pleurisy. The chest X-ray should help sort that out.”\u003c/p>\n\u003cp>Every answer the patient gives, and each positive or negative finding on the physical examination (yes, there is a heart murmur; no, the liver is not enlarged) triggers an automatic, almost intuitive recalibration of the most likely alternatives. When I see a master clinician at work—my favorite is my UCSF colleague Gurpreet Dhaliwal, who was profiled in a 2012\u003ca href=\"http://www.nytimes.com/2012/12/04/health/quest-to-eliminate-diagnostic-lapses.html\" target=\"_blank\"> New York Times article\u003c/a>—I know that these synapses are firing as he asks a patient a series of questions that may seem unrelated to the patient’s presenting complaint but are directed toward “narrowing the differential.” It turns out that there’s an even more impressive piece of cognitive magic going on. The master clinician embraces certain pieces of data (the patient’s trip to rural Thailand last year) while discarding others (an episode of belly pain and bloating three weeks ago). This is the part of diagnostic reasoning that beginners find most vexing, since they lack the foundational knowledge to understand why their teacher focused so intently on one nugget of information and all but ignored others that, to the novice, seemed equally crucial. How do the great diagnosticians make such choices?\u003c/p>\n\u003cp>We now recognize this as a relatively intuitive version of \u003ca href=\"http://www.medicinenet.com/script/main/art.asp?articlekey=10301\" target=\"_blank\">Bayes’ theorem\u003c/a>. Developed by the eighteenth-century British theologian-turned-mathematician Thomas Bayes, this theorem (often ignored by students because it is taught to them with the dryness of a Passover matzo) is the linchpin of clinical reasoning. In essence, Bayes’ theorem says that any medical test must be interpreted from two perspectives. The first: How accurate is the test—that is, how often does it give right or wrong answers? The second: How likely is it that this patient has the disease the test is looking for?\u003c/p>\n\u003cp>These deceptively simple questions explain why, in the early days of the AIDS epidemic (when HIV testing was far less accurate than it is today), it was silly to test heterosexual couples applying for a marriage license, since the vast majority of positive tests in this very low-risk group would be wrong. Similarly, they show why it is foolish to screen healthy 36-year-old executives with a cardiac treadmill test or a heart scan, since positive results will mostly be false positives, serving only to scare the bejesus out of the patients and run up bills for unnecessary follow-up tests. Conversely, in a 68-year-old smoker with diabetes and high cholesterol who develops squeezing chest pain while jogging, there is a 95 percent chance that those pains are from coronary artery disease. In this case, a negative treadmill test only lowers this probability to about 80 percent, so the clinician who reassures the patient that his negative test means that his heart is fine—“take some antacids; it’s OK to keep jogging”—is making a terrible, and potentially fatal, mistake.\u003c/p>\n\u003cp>\u003cstrong>The AI Challenge\u003c/strong>\u003c/p>\n\u003cp>As if this weren’t complicated enough for the poor IBM engineer gearing up to retool Watson from answering questions about “Potent Potables” to diagnosing sick patients, there’s more. While the EHR at least offers a fighting chance for computerized diagnosis (older medical AI programs, built in the pen-and-paper era, required busy physicians to write their notes and then reenter all the key data), parsing an electronic medical record is far from straightforward. Natural language processing is getting much better, but it still has real problems with negation (“the patient has no history of chest pain or cough”) and with family history (“there is a history of arthritis in the patient’s sister, but his mother is well”), to name just a couple of issues. Certain terms have multiple meanings: when written by a psychiatrist, the term depression is likely to refer to a mood disorder, while when it appears in a cardiologist’s note (“there was no evidence of ST-depression”) it probably refers to a dip in the EKG tracing that is often a clue to coronary disease. Ditto abbreviations: Does the patient with “MS” have multiple sclerosis or mitral stenosis, a sticky heart valve? Finally, the computer can’t read a patient’s tone of voice or the anxious look on her face, although engineers are working on this. These clues—like one patient saying, “I have chest pain,” and another, “I HAVE CHEST PAIN!!!”—can make all the difference in the world diagnostically.\u003c/p>\n\u003cp>Perhaps the trickiest problem of all is that—at least today—the very collection of the facts needed to feed an AI system is itself a cognitively complex process. Let’s return to the example of aortic dissection, a rip in the aorta that is often fatal if it is not treated promptly. If the initial history raises the slightest concern about dissection, I’m going to ask questions about whether the pain bores through to the back and check carefully for the quiet murmur of aortic insufficiency as well as for asymmetric blood pressure readings in the two arms, all clues to dissection. If I don’t harbor a suspicion of this scary (and unusual) disease, I’m not going to look for these things—they’re not part of a routine exam.\u003c/p>\n\u003cp>Decades ago, MIT’s Peter Szolovits, an AI expert who worked with Kassirer and his colleagues in the early days, gave up thinking about diagnosis as a simple matter of question answering. This was mostly because he came to appreciate the importance of timing—a nonissue in Jeopardy but a pivotal one in medicine. “A heart attack that happened five years ago has different implications from one that happened five minutes ago,” he explained, and a computer can’t “know” this unless it is programmed to do so. (It turns out that such issues of foundational knowledge are fundamental in AI—computers have no way of “knowing” some of the basic assumptions that allow us to get through our days, things like water is wet, love is good, and death is permanent.)\u003c/p>\n\u003cp>Moreover, much of medical reasoning relies on feedback loops: observing how events unfold and using that information to refine the diagnostic possibilities.We think a patient has bacterial pneumonia, and so we treat the “pneumonia” with antibiotics, but the patient’s fever doesn’t break after three days. So now we consider the possibility of tuberculosis or lupus. This is the cognitive work of the practicing clinician—focused a bit less on “What is the diagnosis?” and more on “How do I best manage this situation?”—and an AI program that doesn’t account for this will be of limited value.\u003c/p>\n\u003cp>\u003cstrong>Early Attempts\u003c/strong>\u003c/p>\n\u003cp>Now that you appreciate the nature of the problem, it’s easy (in retrospect, at least) to see why the choice by early health care computer experts to focus on diagnosis was risky, perhaps even wrongheaded. It’s like tackling Saturday’s crossword puzzle in the New York Times before first mastering the one in USA Today.\u003c/p>\n\u003cp>Larry Fagan, an early Stanford computing pioneer, told me, “We were not naive about the complexity. It’s just that it was the most exciting question.” Diagnosis is not just exciting, it’s at the heart of safe medical care. Diagnostic errors are common, and they can be fatal. A number of autopsy studies conducted over the past 40 years have shown that major diagnoses were overlooked in nearly one in five patients. With the advent of CT scans and MRIs, the number has gone down a bit, but it still hovers around one in ten. Diagnostic errors contribute to 40,000 to 80,000 deaths per year in the United States. And reviews of malpractice cases have demonstrated that diagnostic errors are the most common source of mistakes leading to successful lawsuits.\u003c/p>\n\u003cp>Medical IT experts jumped into the fray in the 1970s, designing a series of computer programs that they believed could help physicians be better diagnosticians, or perhaps even replace them entirely. That decade’s literature was replete with enthusiastic articles about how microprocessors, programmed to think like experts, would soon replace the brains of harried doctors. The attitude was captured by one early computing pioneer in a 1971 paean to his computer: “It is immune from fatigue and carelessness; and it works day and night, weekends and holidays, without coffee breaks, overtime, fringe benefits or human courtesy.”\u003c/p>\n\u003cp>By the mid-1980s, disappointment had set in. The tools that had seemed so promising a decade earlier were, by and large, unable to manage the complexity of clinical medicine, and they garnered few clinician advocates and miniscule commercial adoption. The medical AI movement skidded to a halt, marking the start of a 20-year period that insiders still refer to as the “AI winter.” Ted Shortliffe, one of the field’s longstanding leaders, has said that the early experience with programs like INTERNIST, DXplain, and MYCIN reminded him of this cartoon:\u003c/p>\n\u003cp>\u003ca href=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2016/11/cartoon.jpg\">\u003cimg class=\"aligncenter size-full wp-image-276235\" src=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2016/11/cartoon.jpg\" alt=\"cartoon\" width=\"300\" height=\"373\" srcset=\"https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon.jpg 300w, https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon-160x199.jpg 160w, https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon-240x298.jpg 240w\" sizes=\"(max-width: 300px) 100vw, 300px\">\u003c/a>\u003c/p>\n\u003cp>\u003cstrong>'Version 0'\u003c/strong>\u003c/p>\n\u003cp>Vinod Khosla is prepared for this. He knows that even today’s generation of medical AI programs will produce some crazy output, akin to when Watson famously mistook Toronto for an American city during its Jeopardy triumph. (It was worse in rehearsal, when Watson referred to civil rights leader Malcolm X as “Malcolm Ten.”) Khosla points out that the enormous cellphones of the late 1980s would seem equally ridiculous when placed alongside our iPhone 6.0s. He calls today’s medical AI programs “Version 0,” and cautions that people should “expect these early systems and tools to be the butt of jokes from many a writer and physician.”\u003c/p>\n\u003cp>These cases illustrate a perennial debate in AI, one that pits two camps against each other: the “neats” and the “scruffies.” The neats seek solutions that are elegant and provable; they try to model the way experts think and work, and then code that into AI tools. The scruffies are the pragmatists, the hackers, the crazy ones; they believe that problems should be attacked through whatever means work, and that modeling the behavior of experts or the scientific truth of a situation isn’t all that important. IBM’s breakthrough was to figure out that a combination of neat and scruffy—programming in some of the core rules of the game, but then folding in the fruits of machine learning and natural language processing—could solve truly complicated problems.\u003c/p>\n\u003cp>When he was asked about the difference between human thinking and Watson’s method, Eric Brown, who runs IBM’s Watson Technologies group, gave a careful answer (note the shout-out to the humans, the bit players who made it all possible):\u003c/p>\n\u003cblockquote>\u003cp>A lot of the way that Watson works is motivated by the way that humans analyze problems and go about trying to find solutions, especially when it comes to dealing with complex problems where there are a number of intermediate steps toget you to the final answer. So it certainly is inspired by that process. . . . But a lot of it is different from the ways humans work; it tends to leverage the powers and advantages of a computer system, and its ability to rapidly analyze huge amounts of data and text that humans just can’t keep track of.\u003c/p>\u003c/blockquote>\n\u003cp>However Watson works, we find ourselves today in a world with new tools, new mental models, and a new sense of optimism that computers can do pretty much anything. But have we finally reached the age when computers can master the art of clinical reasoning?\u003c/p>\n\u003cp>I asked Eric Brown, who worked on the \"Jeopardy\" project and is now helping to lead Watson’s efforts in medicine, what the equivalent event might be in health care, the moment when his team could finally congratulate itself on its successes. I wondered if it would be the creation of some kind of holographic physician—like “\u003ca href=\"http://memory-alpha.wikia.com/wiki/Emergency_Medical_Holographic_program\" target=\"_blank\">The Doctor\u003c/a>” on Star Trek Voyager—with Watson serving as the cognitive engine. His answer, though, reflected the deep respect he and his colleagues have for the magnitude of the challenge:\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"floatright"},"numeric":["floatright"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>“It will be when we have a technology that physicians suddenly can’t live without.”\u003c/p>\n\n\u003c/div>\u003c/p>","attributes":{"named":{},"numeric":[]}}],"link":"/futureofyou/274449/will-computers-ever-be-able-to-make-diagnoses-as-well-as-physicians","authors":["byline_futureofyou_274449"],"categories":["futureofyou_452","futureofyou_1","futureofyou_73"],"tags":["futureofyou_849","futureofyou_1105","futureofyou_1439","futureofyou_915","futureofyou_190","futureofyou_1014","futureofyou_80","futureofyou_1106"],"featImg":"futureofyou_274615","label":"source_futureofyou_274449"},"futureofyou_141055":{"type":"posts","id":"futureofyou_141055","meta":{"index":"posts_1591205157","site":"futureofyou","id":"141055","score":null,"sort":[1459983218000]},"guestAuthors":[],"slug":"facebook-rolls-out-photo-recognition-for-blind-users","title":"Facebook Rolls Out Photo Recognition For Blind Users","publishDate":1459983218,"format":"standard","headTitle":"KQED Future of You | KQED Science","labelTerm":{"site":"futureofyou"},"content":"\u003cp>Facebook is training its computers to become seeing-eye guides for blind and visually impaired people as they scroll through the pictures posted on the world's largest online social network.\u003c/p>\n\u003cp>The feature rolled out Tuesday on Facebook's iPhone and iPad apps interprets what's in a picture using a form of artificial intelligence that recognizes faces and objects. VoiceOver, a screen reader built into the software powering the iPhone and iPad, must be turned on for Facebook's photo descriptions to be read. For now, the feature will only be available in English.\u003c/p>\n\u003cp>Until now, people relying on screen readers on Facebook would only hear that a person had shared a photo without any elaboration.\u003c/p>\n\u003cp>\u003c!-- iframe plugin v.4.3 wordpress.org/plugins/iframe/ -->\u003cbr>\n\u003ciframe src=\"https://player.vimeo.com/video/161529744\" width=\"500\" height=\"281\" frameborder=\"0\" scrolling=\"yes\" class=\"iframe-class\">\u003c/iframe>\u003c/p>\n\u003cp>The photo descriptions initially will be confined to a vocabulary of 100 words in a restriction that will prevent the computer from providing a lot of details. For instance, the automated voice may only tell a user that a photo features three people smiling outdoors without adding that the trio also has drinks in their hands. Or it may say the photo is of pizza without adding that there's pepperoni and olives on top of it.\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>Facebook is being careful with the technology, called \"automatic alternative text,\" in an attempt to avoid making a mistake that offends its audience. Google learned the risks of automation last year when an image recognition feature in its Photos app labeled a black couple as gorillas, prompting the company to issue an apology.\u003c/p>\n\u003cp>Eventually, though, Facebook hopes to refine the technology so it provides more precise descriptions and even answers questions that a user might pose about a picture.\u003c/p>\n\u003cp>The vocabulary of Facebook's photo-recognition program includes \"car,\" \"sky,\" \"dessert,\" \"baby,\" \"shoes,\" and, of course, \"selfie.\"\u003c/p>\n\u003cp>Facebook also plans to turn on the technology for its Android app and make it available through Web browsers visiting its site.\u003c/p>\n\u003cp>The Menlo Park, California, company is trying to ensure the world's nearly 300 million blind and visually impaired people remain interested in its social network as a steadily increasing number of photos appear on its service. On an average day, Facebook says more than 2 billion photos are posted on its social network and other apps that it owns, a list that includes Messenger, Instagram and WhatsApp.\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>In a Tuesday post, Facebook CEO Mark Zuckerberg hailed the photo description tool as \"an important step towards making sure everyone has equal access to information and is included in the conversation.\"\u003c/p>\n\n","blocks":[],"excerpt":"The feature on Facebook's iPhone and iPad apps interprets what's in a picture using a form of artificial intelligence that recognizes faces and objects.","status":"publish","parent":0,"modified":1459989867,"stats":{"hasAudio":false,"hasVideo":true,"hasChartOrMap":false,"iframeSrcs":[],"hasGoogleForm":false,"hasGallery":false,"hasHearkenModule":false,"hasPolis":false,"paragraphCount":13,"wordCount":415},"headData":{"title":"Facebook Rolls Out Photo Recognition For Blind Users | KQED","description":"The feature on Facebook's iPhone and iPad apps interprets what's in a picture using a form of artificial intelligence that recognizes faces and objects.","ogTitle":"","ogDescription":"","ogImgId":"","twTitle":"","twDescription":"","twImgId":"","schema":{"@context":"http://schema.org","@type":"Article","headline":"Facebook Rolls Out Photo Recognition For Blind Users","datePublished":"2016-04-06T22:53:38.000Z","dateModified":"2016-04-07T00:44:27.000Z","image":"https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"}},"disqusIdentifier":"141055 http://ww2.kqed.org/futureofyou/?p=141055","disqusUrl":"https://ww2.kqed.org/futureofyou/2016/04/06/facebook-rolls-out-photo-recognition-for-blind-users/","disqusTitle":"Facebook Rolls Out Photo Recognition For Blind Users","nprByline":"Michael Liedkte\u003cbr />Associated Press","path":"/futureofyou/141055/facebook-rolls-out-photo-recognition-for-blind-users","audioTrackLength":null,"parsedContent":[{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003cp>Facebook is training its computers to become seeing-eye guides for blind and visually impaired people as they scroll through the pictures posted on the world's largest online social network.\u003c/p>\n\u003cp>The feature rolled out Tuesday on Facebook's iPhone and iPad apps interprets what's in a picture using a form of artificial intelligence that recognizes faces and objects. VoiceOver, a screen reader built into the software powering the iPhone and iPad, must be turned on for Facebook's photo descriptions to be read. For now, the feature will only be available in English.\u003c/p>\n\u003cp>Until now, people relying on screen readers on Facebook would only hear that a person had shared a photo without any elaboration.\u003c/p>\n\u003cp>\u003c!-- iframe plugin v.4.3 wordpress.org/plugins/iframe/ -->\u003cbr>\n\u003ciframe src=\"https://player.vimeo.com/video/161529744\" width=\"500\" height=\"281\" frameborder=\"0\" scrolling=\"yes\" class=\"iframe-class\">\u003c/iframe>\u003c/p>\n\u003cp>The photo descriptions initially will be confined to a vocabulary of 100 words in a restriction that will prevent the computer from providing a lot of details. For instance, the automated voice may only tell a user that a photo features three people smiling outdoors without adding that the trio also has drinks in their hands. Or it may say the photo is of pizza without adding that there's pepperoni and olives on top of it.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"fullwidth"},"numeric":["fullwidth"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>Facebook is being careful with the technology, called \"automatic alternative text,\" in an attempt to avoid making a mistake that offends its audience. Google learned the risks of automation last year when an image recognition feature in its Photos app labeled a black couple as gorillas, prompting the company to issue an apology.\u003c/p>\n\u003cp>Eventually, though, Facebook hopes to refine the technology so it provides more precise descriptions and even answers questions that a user might pose about a picture.\u003c/p>\n\u003cp>The vocabulary of Facebook's photo-recognition program includes \"car,\" \"sky,\" \"dessert,\" \"baby,\" \"shoes,\" and, of course, \"selfie.\"\u003c/p>\n\u003cp>Facebook also plans to turn on the technology for its Android app and make it available through Web browsers visiting its site.\u003c/p>\n\u003cp>The Menlo Park, California, company is trying to ensure the world's nearly 300 million blind and visually impaired people remain interested in its social network as a steadily increasing number of photos appear on its service. On an average day, Facebook says more than 2 billion photos are posted on its social network and other apps that it owns, a list that includes Messenger, Instagram and WhatsApp.\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>In a Tuesday post, Facebook CEO Mark Zuckerberg hailed the photo description tool as \"an important step towards making sure everyone has equal access to information and is included in the conversation.\"\u003c/p>\n\n\u003c/div>\u003c/p>","attributes":{"named":{},"numeric":[]}}],"link":"/futureofyou/141055/facebook-rolls-out-photo-recognition-for-blind-users","authors":["byline_futureofyou_141055"],"categories":["futureofyou_452","futureofyou_1","futureofyou_73"],"tags":["futureofyou_849","futureofyou_178","futureofyou_850"],"featImg":"futureofyou_141062","label":"futureofyou"}},"programsReducer":{"possible":{"id":"possible","title":"Possible","info":"Possible is hosted by entrepreneur Reid Hoffman and writer Aria Finger. Together in Possible, Hoffman and Finger lead enlightening discussions about building a brighter collective future. The show features interviews with visionary guests like Trevor Noah, Sam Altman and Janette Sadik-Khan. Possible paints an optimistic portrait of the world we can create through science, policy, business, art and our shared humanity. It asks: What if everything goes right for once? How can we get there? 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You can also visit the MindShift website for episodes and supplemental blog posts or tweet us \u003ca href=\"https://twitter.com/MindShiftKQED\">@MindShiftKQED\u003c/a> or visit us at \u003ca href=\"/mindshift\">MindShift.KQED.org\u003c/a>","imageSrc":"https://cdn.kqed.org/wp-content/uploads/2024/04/Mindshift-Podcast-Tile-703x703-1.jpg","imageAlt":"KQED MindShift: How We Will Learn","officialWebsiteLink":"/mindshift/","meta":{"site":"news","source":"kqed","order":"2"},"link":"/podcasts/mindshift","subscribe":{"apple":"https://podcasts.apple.com/us/podcast/mindshift-podcast/id1078765985","google":"https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5tZWdhcGhvbmUuZm0vS1FJTkM1NzY0NjAwNDI5","npr":"https://www.npr.org/podcasts/464615685/mind-shift-podcast","stitcher":"https://www.stitcher.com/podcast/kqed/stories-teachers-share","spotify":"https://open.spotify.com/show/0MxSpNYZKNprFLCl7eEtyx"}},"morning-edition":{"id":"morning-edition","title":"Morning Edition","info":"\u003cem>Morning Edition\u003c/em> takes listeners around the country and the world with multi-faceted stories and commentaries every weekday. 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