Comic Strips

They don't use the generative models for this. The AI's that do this kind of work are trained on carefully curated data and have a very narrow scope that they are good at.

Image categorisation AI, or convolutional neural networks, have been in use since well before LLMs and other generative AI. Some medical imaging machines use this technology to highlight features such as specific organs in a scan. CNNs could likely be trained to be extremely proficient and reading X-rays, CT, MRI scans, but these are generally the less operator dependant types of scan, though they can get complicated. An ultrasound for example is highly dependent on the skill of the operator and in certain circumstances things can be made to look worse or better than they are.

I don't know why the technology hasn't become more widespread in the domain. Probably because radiologists are paid really well and have a vested interest in preventing it... they're not going to want to tag the images for their replacement. It's probably also because medical data is hard to get permission for, to ethically train such a model you would need to ask every patient in for every type of scan it their images can be used for medical research which is just another form/hurdle to jump over for everyone.

It's certainly not as bad as the problems generative AI tend to have, but it's still difficult to avoid strange and/or subtle biases.

Very promising technology, but likely to be good at diagnosing problems in Californian students and very hit-and-miss with demographics which don't tend to sign up for studies in silicon valley

Basically AI is generally a decent answer to the needle in a haystack problem. Sure, a human with infinite time and attention can find the needle and perhaps more accurately than an AI could, but practically speaking if there's just 10 needles in a haystack it's considered a lost cause to find any of them.

With AI it might find in that same stack 30 needles, of which only 7 of them are the needles, which means the AI finds more wrong answers than right, but ultimately you do end up finding 7 needles when you would have missed all 10 before, coming out ahead.

So long as you don't let an AI rule out review of a scan that a human really would have reviewed, it seems a win to potentially have more overall scans get a decent review and maybe catch things earlier in otherwise impractical preventative scans