The IBM Whiz Kid's Work on Cancer

Watson's foray into oncology is only the first baby step toward applying "big data" to thorny medical problems.

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Can IBM's supercomputer become the ultimate medical assistant?

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Watson the supercomputer was last seen on "Jeopardy!" in 2011, crushing two very human former champions and taking home the $1 million prize. Now IBM's whiz kid (whose winnings went to charity) has moved beyond trivia to an even tougher question: Can a really smart computer become the ultimate physician's assistant and vastly improve the quality of health care?

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Mark Kris, chief of thoracic oncology at Memorial Sloan-Kettering Cancer Center, thinks so, and he demonstrates how using the case of a virtual patient, Lin Yamato, a 37-year-old woman who never smoked but who recently received a diagnosis of advanced lung cancer. Even before Yamato visits her cancer specialist, Watson goes to work, culling details of her test results and medical history from her electronic medical record and analyzing them in the context of the 2 million pages of knowledge it's already been fed: journal articles, clinical trial results, treatment guidelines, textbooks and the histories of hundreds of de-identified Sloan-Kettering patients.

Based on the evidence, Watson suggests more tests – a molecular analysis of the tumor and an MRI of the brain – and brings up the relevant references. And the computer offers a preliminary assessment of three treatment plans. At this stage, there's no clear leader; the computer's confidence in each plan hovers around the 30 percent mark. It says it needs more information.

That judgment is the crux of Watson's role. The computer "doesn't tell you what to do. It tells you that a treatment is at the top of the list" for a particular patient, says Kris. What makes Watson unique, besides its power (on "Jeopardy!" it sifted through more than 66 million pages of data per second and has only gotten faster since), is its ability to absorb text that isn't in a standardized format, such as a doctor's case notes, and pull out the most relevant ideas. And it learns from experience, says Marty Kohn, IBM's chief medical scientist; the more cases Watson sees, and the more interaction it has with users who point out additional sources Watson should consider, for instance, the better it gets. "It's like teaching someone to be a doctor," says Kris.

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Watson's foray into oncology is only the first baby step toward applying "big data" to thorny medical problems. By one estimate, health information – electronic health records, insurance claims, images such as CT scans, vital signs of people being remotely monitored by hospitals or smartphone, gene sequencing results – will grow to the equivalent of about 500 billion four-drawer file cabinets by 2020, from a mere 10 billion in 2011. High-powered computers and new algorithms have the potential to allow physicians and researchers to combine and decipher all that information and see what connections pop. Ideally, medicine would then be able to better track and predict the spread of disease, and diagnose, treat and prevent it, all while improving safety and lowering costs.

"You can look back and see what happened historically. You can do a real-time analysis of that data, and you can look forward: How can I use this to predict what will happen?" says C. Martin Harris, chief information officer of the Cleveland Clinic, which is working with Watson to improve medical education. At the moment, Watson is "learning" the same kind of information as trainee physicians, with the goal of taking the same licensure exam they take. Once up to snuff, the computer will turn the tables and help the students.

There's clearly great potential in crunching all this data, but also many obstacles. For starters, this kind of computer power can require big upfront investments. The Icahn School of Medicine at Mount Sinai in New York spent $3 million on Minerva, its supercomputer to be used for genomic and other research, for example.

Another challenge: Before meaningful interpretation of the information can begin, all sorts of disparate data sources must be brought together, no small feat in an industry where patient privacy and security are paramount and the culture is oriented toward not sharing data. Efforts to overcome that tradition are underway. The American Society of Clinical Oncology, for example, is developing a database intended to pool clinical information on hundreds of thousands of cancer patients for use by oncologists. And several electronic health record vendors recently announced an agreement to ease the flow of data between their systems.


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