Important study to do, but feel like I have to assert that in principle, ML is not as useful for diagnosis as it is for aggregating longitudnal data because the

Important study to do, but feel like I have to assert that in principle, ML is not as useful for diagnosis as it is for aggregating longitudnal data because the consequences of a false positive/negative in the individual diagnosis are life threatening, and even if it catches something doctors don't, it just means a doctor has to look at them. However, across a population, nobody is being harmed by the ML analysis, and it can pick out patterns a person could not at speed and at scale. Spending research to get your individual diagnostic ROC curve a few percent higher is a waste of time if the consequences of it being wrong are greater than the time savings it provides. It's fine for veterinary medicine, and maybe prisoners and keeping people with hidden symptoms out of crowded places, but for regular people, using ML has got a host of ethical problems that aren't being dealt with at the policy level. You can use rules engines (literally, DROOLS) instead of non-deterministic ML schemes to do diagnoses and prescriptions. Have said before, ML is only useful for problems with asymmetric upside, and it is worse than bad for ones with asymmetric downside.