Comment by rapjr9

Good wishlist, but how do you measure risk in a trained ML algorithm? What does reliability and stability mean? How do you measure bias? How much accuracy in results is enough for a specific purpose? These are all still research questions. Requiring diversity in training data inputs helps, but it is no guarantee of a fair or correct outcome, and there will likely always be weird edge cases where the algorithms go off the rails. Do you need to include Zulu people, Inuit people, and Hawaiian people in large proproportions in your training data? Maybe you need to train a different algorithm for each comorbidity a patient might present with? And all the combinations of those comorbidities? Does your training data have to include all the environmental factors that might affect the outcome? "Personalized medicine" recognizes that all people are unique and different in medically important ways, yet machine learning has a basic assumption that all people are the same and you can just dump data from everyone into the training and the algorithm will figure it out...somehow. This is unproven and likely just plain wrong.