[not the gp] the big problem with ML in medicine is that the experts of the two fields lack the - in both cases very deep - domain expertise of the other field

[not the gp] the big problem with ML in medicine is that the experts of the two fields lack the - in both cases very deep - domain expertise of the other field. Ask yourself much much top talent in ML there really is, now ask yourself how many of those work on a specific problem or toolset in medicine. Then ask yourself how much top talent in the medical fields has a profound intersection with statistics. Both are deep fields dealing with inherently complicated problem spaces. The amount of people who are able to make profound contributions to both fields can probably fit in a medium-sized conference room, while there are hundreds, if not a magnitude more, ML-driven medical research projects. It's hard enough making contributions to either field without heavily intersecting with a completely different domain, and a lot of the best-in-class experts of ML (or, better: AI (!)) are busy solving things like protein folding in London. And if you look at those guys faces, they look tired, because what they are doing is really, really hard.