>Why isn't this already is practice given ML advances in recent years? Well it would be nice to first validate the results somewhere else (peer review should a

>Why isn't this already is practice given ML advances in recent years? Well it would be nice to first validate the results somewhere else (peer review should actually be completed) and no doubt some real world trials proceed and their results evaluated. This is what any FDA approval, at a minimum, would entail. Keep in mind what the author's themselves note in how this study may differ from the "real world": "One limitation of this study is that cases submitted for TCGA and TMA databases might be biased in terms of having mostly images in which the morphological patterns of disease are definitive, which could be different from what pathologists encounter at their day-to-day practice." Putting this into practice also requires looking at the bigger picture; not just the accuracy of the diagnosis vs. humans. For example I have found that in studies doctors are much more conservative in their diagnosis than an academic algorithm with no implication on patient outcome. For instance if the question is a) "cancer" or b) "not cancer" the doctor, fearing patient harm, malpractice suits, career derailment, will be biased to identifying "a) cancer" because the perceived costs of a false positive (treating for a nonexistent cancer which may have significant ill effects) are lower than a false negative (not treating a real cancer leading potentially to death). This will reduce the human doctor's accuracy on diagnosis. What the "right" bias is in an individual case can bring in many more factors and moral questions out of scope for this study. This is not to argue that tools like these should not be developed and utilized but practical application is often more difficult than just solving the technology challenge. I'm certain that we'll see many advances in healthcare due to ML.