One way I tend to view ML is that, when its wrong, its
catastrophically wrong. Because it doesn't actually
understand anything its doing, and simply looking at a probability model and picking based off it, you end up with the issue that a few carefully selected pixel changes in a picture of a cat takes the model from a cat to an ostrich.
The model does not see a significant difference between the cat/ostrich, or cancer/cold, whereas we do; this implies that, when the model is wrong, it is likely to not just provide an incorrect treatment, but a catastrophically incorrect treatment.
Where the human sees a cat-like creature, and if not guess a cat, then something similar to a cat (4 legged, furry, etc), the ML model is willing to jump anywhere, in the worst case.
So its not just rate of misdiagnosis, but by how much as well.