Modern medicine already incorporates wide ranges of data. Doctors use flowcharts, scales, point systems, etc, to diagnose certain conditions because those tools have been developed by studying and considering a lot of cases.
However, there's a lot that isn't covered with data. The "middle of the scale", the "almost but not quite there", the "this is weird"... Doctors are good at that, through experience, and those are the difficult cases. Those are the ones where ML will not only likely fail, but won't even explain why it fails. We're talking about human lives here. If anything, I think software engineers massively overestimate the performance of ML and underestimate doctors.
Yes, notwithstanding those factors you described, it is not uncommon for tests to reveal false-negative or false-positive results due to their intrinsic specificity and sensitivity. A normal value is not always indicative of health, either.
However, there's a lot that isn't covered with data. The "middle of the scale", the "almost but not quite there", the "this is weird"... Doctors are good at that, through experience, and those are the difficult cases. Those are the ones where ML will not only likely fail, but won't even explain why it fails. We're talking about human lives here. If anything, I think software engineers massively overestimate the performance of ML and underestimate doctors.