You can do some stuff that helps people who aren't familiar with the math. E.g. you can color in the pixels that most prominently went into making a decision. If the 'relevant' pixels are nowehere near the lesion then that's a pretty good indication that the AI is telling BS.

Another idea that is being explored is that it will select some images from the training set that it thinks show a similar pathology (or not) and display those alongside.

Problem isn't so much that AI makes mistakes (anyone can forgive it that if the overall result is net positive). The main problem is that it makes different mistakes than humans...i.e. you're running the risk of overlooking something that a human would have easily spotted if you overrely on AI diagnstics.

Well, I'm drawing the analogy to Tesla. While one can have a viable product in a much shorter timespan: in order to reap real economies of scale type benefits (i.e. what will set the winner apart from the 'also-ran' competition that will eventually go bankrupt because they cannot offer a competitive product at a similar price ) you have to go big. And I mean: REALLY big. Large factories. Global resource chains. That takes time.

any link for any actual data?

Similar with self-driving cars, they may work with (made up number) 99% accuracy, but that 1% is still too risky.

Regardless of what the AI says, I would still ask a doctor to see my scan considering a false-negative could cost me my life and a false-positive would probably mean a doctor would double check it anyway.

The bottleneck would still be the person who looks at each scan personally.

That being said, I think there is huge potential in early risk prediction using ML long before a real human could even spot cancer tissue.

It isn’t so easy. I mean, you can make something that works more or less. But moving it to a level where it can really be useful clinically will require a huge effort. Because you need to work at extremely low false positive rates while being very sensitive at detection. And, although cancer can be quite obvious in diagnostic scans, in screening scans they are usually very subtle (because screening is done without any symptoms). It will be like looking for a needle in the haystack. And try doing this in that sensitivity/specificity requirements.

I think in general the problem with engineers working on medical topics is that they underestimate the complexity of the problems and the requirement to have in-depth insights about the problems they work on. I get a similar impression from your posts.

IMO the biggest problem is not the ML side, it's the people-workflow-deployment side (as other commenters have pointed out) -- a radiologist has spent 18 years of schooling plus a residency, how can you position AI to help them do their job better rather than presumptively challenge their expertise? The cost of error (particularly false negatives) is orders of magnitude higher than the benefit of getting the vast majority of true-negatives correct, what's the right way to tune the model?

Also, based on my experience in the space, there's plenty of training data out there, but outside of routine preventive scans like mammograms, many diagnoses don't have a lot of "clean" negative examples, because doctors won't order a CT unless they have reason to suspect something is wrong. This is unlikely to change, since the radiation exposure from the scan poses individual harm to the patient without providing individual benefit.

That's a different use case. I work in healthcare (computer vision solutions) and I have yet to see a use-case for text-to-video generation.

So far most of the use-cases are either: classification or segmentation, mostly for diagnosing diseases. There's also biomedical informatics, but I'm not familiar enough with medical tabular data to comment on it.