This can be dangerous. A lot of medical imaging deliberately avoids using any kind of lossy compression due to worries about artifacts in the image. Actually adding new pixels that are not in the raw image seems especially worrying.
https://petapixel.com/2020/08/17/gigapixel-ai-accidentally-a...
I've mentioned this before[0], so quoting myself:
"for example, a research team may decide to not spend money on expensive scientific cameras for monitoring experiment, and instead opt to buy an expensive - but still much cheaper - DSLR sold to photographers, or strap a couple of iPhones 15 they found in the drawer (it's the future, they're all using iPhones 17, which is two generations behind the newest one). That's using COTS equipment. COTS is typically sold to less sophisticated users, but is often useful for less sophisticated needs of more sophisticated users too. But if COTS cameras start to accrue built-in algorithms that literally fake data, it may be a while before such researchers realize they're looking at photos where most of the pixels don't correspond to observable reality, in a complicated way they didn't expect."
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Up-sample your Tinder photo? Sure.
Look for a sarcoma or bulging disk? No
When it comes to things like distinguishing a shadow on a scan, I think AI might actually be better 'detecting' whether something is a real shadow or just very similar to a shadow. I think it's just one of those things where AI up-scaling improves stuff ~80% of the time but is worse the other ~20%. The fundamental issue may become the same with self driving cars; people trust the AI too much and become inattentive themselves.
While you certainly can't add 'correct' information that doesn't already exist in an image, the upscaling could correctly make existing information more obvious. Assuming that the human brain functions pretty much like AI (or rather the opposite) then at some point AI will become as competent which means that eventually with enough training & tweaking it should be as good or better than having a second human perspective.
It could actually be useful for compression as a predictor, but only if you also store the residual so that the true original image can be reconstructed.
Pretty scary stuff.
Depending on these numbers it could be used as a screener test for example, where it is used before a more invasive test is done.
I'm not a doctor, but I am a physicist and former pro-photographer, what is noise and what is signal in an experiment whose output is an 'image' has nothing to do with what makes a photo look good to human eyes. Often the whole point of methods of visualization is to make the image look objectively bad so you can easily pick out the areas of interest by the fact they are an eyesore. Applying upscaling to those images will actively destroy vital data.
Imagine that thing being removed or enhanced by some algorithm.
Also, why the heck would medical images want to be upscaled?
One reason might be to reduce eyestrain.
I'm not sure I trust people to maintain that discipline.
It may not be exactly as dangerous as the opposite (doctor looks at image thinks there is something suspicious, checks upscaled image to see if it's there as well), but it's still very dangerous.