A Reality Check for IBM’s AI Ambitions

IBM may have overhyped its Watson machine-learning system, but the company still could have the best access to the kind of data needed to make medicine much smarter.

From my article in the July/August 2017 issue of MIT Technology Review

Paul Tang was with his wife in the hospital just after her knee replacement surgery, a procedure performed on about 700,000 people in the U.S. every year. The surgeon came by, and Tang, who is himself a primary-care physician, asked when he expected her to be Watsonback at her normal routines, given his experience with patients like her. The surgeon kept giving vague non-answers. “Finally it hit me,” says Tang. “He didn’t know.” Tang would soon learn that most physicians don’t know how their patients do in the ordinary measures of life back at home and at work—the measures that most matter to patients.

Tang still sees patients as a physician, but he’s also chief health transformation officer for IBM’s Watson Health (see “50 Smartest Companies 2017.”) That’s the business group developing health-care applications for Watson, the machine-learning system that IBM is essentially betting its future on. Watson could deliver information that physicians are not getting now, says Tang. It could tell a doctor, for instance, how long it took for patients similar to Tang’s wife to be walking without pain, or climbing stairs. It could even help analyze images and tissue samples and determine the best treatments for any given patient.

But lately, much of the press for Watson has been bad. A heavily promoted collaboration with the M.D. Anderson Cancer Center in Houston fell apart this year. As IBM’s revenue has swooned and its stock price has seesawed, analysts have been questioning when Watson will actually deliver much value. “Watson is a joke,” Chamath Palihapitiya, an influential tech investor who founded the VC firm Social Capital, said on CNBC in May.

But if Watson has not, as of yet, accomplished a great deal, one big reason is that it needs certain types of data to be “trained.” And in many cases such data is in very short supply or difficult to access. That’s not a problem unique to Watson. It’s a catch-22 facing the entire field of machine learning for health care. Read more

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