I think automating diagnosis is the least effective and highest risk approach when it comes to creating models for the clinic. The real value for ML in the cli

I think automating diagnosis is the least effective and highest risk approach when it comes to creating models for the clinic. The real value for ML in the clinic is to create or refine signals for physicians from which they might better make a diagnosis. Instead of a model rolling dice and saying "I think this is a malignant nevi", it'd be far more useful for it to say something like "here are 10 other nevi in your hospital which I think are similar and share so-and-such characteristics that ultimately resulted in negative outcomes" or "here's a distribution of likely outcomes I foresee occurring, here are the aspects of the lesions that lead me to believe so". ML in the clinic (and in most domains imho) ought to be a tool to increase consistency and efficiency of practitioners, not replace them.