Another medical student here, also with a very similar ML/programming background. Like the other commenter, I couldn't agree more with your analysis of the main

Another medical student here, also with a very similar ML/programming background. Like the other commenter, I couldn't agree more with your analysis of the main issues plaguing current DL applications in medicine. When I was working on deep learning projects as a undergrad before coming to medical school, I naively assumed that solving a simple image classification problem for cancer detection would be enough. However, as one learns in medical school, imaging is only one component of an entire clinical vignette. Even before a patient undergoes a specific test, the history and physical exam really drive the initial protocols. Sometimes, without having this background knowledge, the classification from a simple program has no utility. A radiologist or pathologist typically has access to this information and can interpret this in the context of the clinical suspicion being put forth. I still believe AI has a role to play in easing this workflow, but "replacing" physicians will take a lot more than detecting some disease "better" or more "accurately" than a physician.