This is more of a research PR piece than anything.
The worst case scenario is that the doctor disagrees with the findings and continues investigation. The best case scenario is that you hit the nail on the head much more quickly than if it was an entirely manual process.
The more complex a tool is, the more likely it is to have flaws. Is it any surprise that the medical industry is slow to trust tools where the feared negative outweigh the few clear positives?
I would rather trust a robot to operate on me rather than diagnose me. The human will be able to adapt and communicate to me while doing so.
Doctors are a social conduit for understanding symptoms, explaining the reasoning behind eventual diagnosis, and being able to perform it themselves. A computer would not be, and certainly not in a way a distraught patient could communicate with easily. I also don't envision computers being tied to writing prescriptions any time soon, which is the other reason I would use a doctor.
Again, surgeries I would be fine with.
Because WebMD can't write you a prescription.
Using terms like "AI" is exactly the kind of behavior that leads to doctors not trusting it. We should be teaching doctors why stats are a valuable tool, not to trust a black box because of "AI". How do you know when to question it?
I can't seem to find the paper I'm thinking of in some quick Google Scholar searching, but I believe there was a follow-up article that looked into why such a fairly simple system was able to beat humans, when it clearly lacked the range of expertise of the human experts (and was entirely missing information on some real conditions). The paper, if I'm remembering correctly, concluded that the win was almost entirely due to one specific failing displayed by the humans (even expert specialists) but not shared by computers: very bad intuition for conditional probabilities.
> Dawes subsequently gathered a large body of evidence showing that human experts did not perform as well as simple linear models at clinical diagnosis, forecasting graduate students’ success, and other prediction tasks (Dawes, 1979; Dawes, Faust, & Meehl, 1989).
Given that a majority of people (and doctors) fail even the simplest probabilistic reasoning tests, this is not particularly surprising. [2]
> We know from several studies that physicians, college students (Eddy, 1982), and staff at Harvard Medical School (Casscells, Schoenberger, & Grayboys, 1978) all have equally great difficulties with this and similar medical disease problems. For instance, Eddy (1982) reported that 95 out of 100 physicians estimated the posterior probability p(cancer|positive) to be between 70% and 80%, rather than 7.8%
1: http://opim.wharton.upenn.edu/risk/library/WPAF201410-Algort...
2: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.128...