an image recognition model like this is usually tuned specifically to have a very low false negative (well below human, often) in exchange for a high false positive rate (overly cautious about cancer)!

This is exactly what is being done. My eldest child is in a Ph. D. program for human - robot interaction and medical intervention, and has worked on image analysis systems in this field. They're intended use is exactly that - a "first look" and "second look". A first look to help catch the small, easily overlooked pre-tumors, and tentatively mark clear ones. A second look to be a safety net for tired, overworked, or outdated eyes.

You in QA?

Nice comment. I like the detail.

For me, the main takeaway doesn't have anything to do with the details though, it's about the true usefulness of AI. The details of the implementation aren't important, the general use case is the main point.