Probably. The following applies to the US: It seems to me that some of the biggest barriers involve getting access to enough data to make a good training set. Some of this is related to HIPAA and related privacy laws, which are in place for a good reason but are still a major barrier. The other big factor is the fragmentation of the data across different, non-interoperable, non-standard formats from different vendors who have intentionally made interoperability difficult. That part bothers me much more. Many patients have data spread across a dozen or more paper charts, lab systems, EMRs, pharmacy databases, etc. As with most data science tasks, the "data munging" is the hardest part.
I care about this mostly from the perspective of treating patients. Beyond training sets for AI, I need those records for the same reason: to make appropriate and informed treatment decisions.
From the outside looking in, it's the kind of situation that begs companies to over-promise on results to get enough money to even give it a shot.
