I agree. It might be useful as an aid, but not as a final diagnosis. For example, maybe machine learning is able to discover some hitherto-unknown correlation between two seemingly unrelated conditions. That could be used as an aid in diagnosis and treatment.

For example, imagine a machine learning algorithm spat out a conclusion, "male patients of South Asian ancestry with a diagnosis of bipolar disorder have a 50% increased chance of later receiving a diagnosis of testicular cancer."

I chose these criteria off the top of my head, so they're meaningless. But bipolar disorder and testicular cancer are two diagnosis that have seemingly very little connection, and it would be even more counter-intuitive if this only significantly affected South Asian men. So it's the kind of correlation that would be very unlikely to be found through any other method than big machine learning studies. But biology is complicated, and sometimes very nonintuitive results do occur.

If this result was produced, and it was later confirmed by follow-up work, then it could be used as a diagnostic tool. Maybe South Asian men who have bipolar disorder need to be checked more often for testiclular cancer. But you would be crazy to assume that just because a South Asian man is bipolar, that they automatically also must have testicular cancer, or vice versa.