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A terrific article in The New York Times Magazine this summer described the decade-long effort on the part of IBM artificial intelligence researchers to build a computer that can beat humans in the game of “Jeopardy!” Since I’m not a computer scientist, their pursuit struck me at first as, well, trivial. But as I read the story, I came to understand that the advance may herald the birth of truly usable artificial intelligence for clinical decision-making.
And that is a big deal.
I’ve lamented, including in an article in this month’s Health Affairs, on the curious omission of diagnostic errors from the patient safety radar screen. Part of the problem is that diagnostic errors are awfully hard to fix. The best we’ve been able to do is improve information flow to try to prevent handoff errors, and teach ourselves to perform meta-cognition: that is, we can think about our own thinking, so that we are aware of common pitfalls and catch them before we pull our diagnostic trigger.
These solutions are fine, but they go only so far. In the age of Google, you’d think we’d be on the cusp of developing a computer that is a better diagnostician than the average doctor. Unfortunately, computer scientists have thought we were close to this same breakthrough for the past 40 years and both they and practicing clinicians have always come away disappointed. Before getting to the Jeopardy-playing computer, I’ll start by recounting the generally sad history of artificial intelligence (AI) in medicine, some of it drawn from our chapter on diagnostic errors in Internal Bleeding:
