* Procure patient medical files. This rabbit hole includes things like HIPPA compliance and sufficiently anonymizing the files. Also, hospitals consider 3 years of information on 100 patients to be a lot of data.
* Have trained medical professionals annotate the files. Good luck convincing doctors to do this. Also good luck in determining what to do if the handful of doctors you're granted a few hours with a week can't agree on annotations.
* Actually produce a model that is reasonably good at pulling information out of patient charts,
Even after all the above, you still only have a solution that is giving you structured data, which is something that other EMRs can provide w/o the problems of accuracy & completeness of NLP process. There is still the process of diagnosis and treatment and also the fact that this is all a loop ('active learning').
https://www.quora.com/Why-isn%E2%80%99t-machine-learning-mor...
Skip to Jae Won Joe's answer to see a case study with a patient showing the interactive process. Then, especially look at ending questions about missing or deceptive data. Seems like their good diagnoses comes from a combination of domain data and expertly reading people in front of them. Machines suck at the part I emphasized. The data sets might not reflect a lot of stuff like that, too. Who knows.
It would take a paradigm shift before AI became useful. Going to watson.com and getting a prescription for antibiotics would be useful and technically feasible. So would augmenting lower trained people to be able to deliver more care. Neither are possible legally and it's not exactly a field you can get away with disregarding the law.