The benefit of medical-grade "AI" (in particular, multi-layered convolution neural networks) is that, given an aggregate of information (say, if you equipped every derm and oncologist with high resolution cameras and a set of parameters to standardize each datum), a trained professional[1] would be able to use that corpus as a very useful resource (used, obviously, in conjunction with their formal training and years of medical experience).
That being said - this is a question you should really be asking those who practice medicine or are actively in research for a living. Go pick up the last years issues of Nature Methods to see what problems they're encountering, and which technological gaps[2] (if any) they may have where YC AI might be applicable.
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[1] Needless to say, this is something I'd be very reluctant to release into the populace's hands, lest you have someone whip together some node.js backend and React iOS app :: "Do I have Skin Cancer? $10 to find out!". One shudders to think what sort of hysteria might follow.
[2] You would be surprised at how technologically adept some of the members of those research teams are -- within a month of DeepMind making the headlines on our tech blogosphere, I was seeing RNN being applied to their industry.
I've had this discussion with my SO (who is a doctor) many times, she strongly believes the breadth+depth of knowledge required for such an AI would be too great, but perhaps as a tool for GPs, or for specific, easy ailments (i.e. telling a patient what they don't have, and if it's serious enough to raise the issue to an actual doctor).
Anecdotal, but I had a suspicious mole looked at, the doctor couldn't decide, got a second opinion from their colleague, he still was only 90% sure. And that's a relatively simple example. Doctors are some of the smartest/hard working people in society, and if they can still make mistakes, medical-grade AI is a long way off.