I read both articles you linked, but I'm not really seeing how they support your point. The first article seemed to support the idea that healthcare staff would welcome more seamless, user-friendly AI tools in the field and the second discussed biases within tools they selected for cancer diagnoses and a tool they used to reduce those biases. Am I misunderstanding what you're saying somewhere?

Also, with regard to the reduction in diagnostic accuracy of diagnosticians with AI, I would need to see the specific article to be sure, but if it's the one that was posted across reddit a few months back, I read through that one as well. It seemed to agree with a similar article about students writing papers with and without the use of ChatGPT (group A writes with it, group B writes without it, and afterwards they are asked to both write without the LLM. Group B's essay was shown to be better. This is a hugely reductive description of the experiment, but gets the idea across). Again, it makes sense that if you use a tool to facilitate an action, that tool is replacing that skill and you get "rusty". It does not mean that the existence of a tool would reduce skill in those who do not use it, though. My suggestion of using it as a screening tool wouldn't affect the diagnostician's skill unless they also used it, which sorta defeats the purpose of them being a human check on the process, post-screening flag.

I can't speak to your other points as they're hypothetical. Obviously, I wouldn't advocate for an inaccurate tool that causes an already overworked field to take on more work. I'm only suggesting that ML is a tool that has use-cases and can be used to supplement current processes to improve outcomes. They can, and are, being improved constantly. If they're employed thoughtfully, I just think they can be a huge benefit.