I am a practicing pathologist and I have seen many attempts and publications to use ML in pathology, which all lack in these aspects: 1. ML is trained on simpli

I am a practicing pathologist and I have seen many attempts and publications to use ML in pathology, which all lack in these aspects: 1. ML is trained on simplified sets (preselected ROIs, limited choice of diagnoses), 2. ML is biased by the experts who labeled learning sets, 3. there is no obvious process of learning from failures after initial training, 4. who is responsible in case of ML error with substantial consequences for patient? The first point is especially for the lack of better word.. wishful. In the daily practice we are used to account for "things unexpected" - non-representative biopsies, parasites in tissue where tumor was suspected, foreign body reaction from previous operations, laboratory accidents (such as swapped paraffin blocks of two patients), and so on (the list is much longer). We deal with it. That ML can discern between 5 most common diagnoses is fine, but it is rather narrow problem to solve.