The issue with AI systems that I've seen and evaluated, (and I've seen just about all of the best ones), is that they are good at finding what a radiologist would find, with the added benefit of finding a few that a radiologist would miss.
Here's the problem, AI finds a few that "A" radiologist would miss, but multiple radiologists would not miss. What I mean by that is that the nature of the misses are somewhere between 'facepalm' and 'other radiologists give rad that missed pathology a slap upside head'. Essentially, the pathology that the AI found but the rad missed is obvious, and the rad says, "Oh yeah! I should have caught that." The sort of misses that might be caused by fatigue, or flipping through too many studies too quickly.
What you really want is AI that can synthesize a set of studies and catch things that 99.999% of radiologists on the planet would never catch. Essentially, things not reflected in the datasets, and things that have not been discovered. You need that because most delivery facilities already outsource image reading to a centralized radiology reading facility that may be miles away. So, financially, they are already saving the money of not paying radiologists. So any centralized radiology reading facility powered by AI, would need to be better than any centralized radiology reading facility powered by humans.
Despite claims made everywhere from papers at RSNA to marketing materials from Silicon Valley, every AI I've seen has failed this test. In both accuracy, as well as the more esoteric metric of finding things that it's not trained on.
[Just as a matter of full disclosure, I have a research background in medical imaging, and have actually had a startup exit in the field.]