Even that is inventing data, no?

Denoising can on average improve the result, but sometimes it will be wrong.

Spotting when it goes wrong is potentially a difficult task, but generally the difficulty scales pretty clearly with the difficulty of understanding the original image anyway. If you can't spot when a denoising filter has screwed up, chances are you wouldn't have spotted anything interesting in the original image anyway.

But once an AI is context-aware things get way more complicated - it will try very hard to produce an image that doesn't _look_ wrong. Even if it goes wrong, it can go wrong and still succeed in managing to make an image that looks correct, it just no longer matches the real brain that was scanned. Perhaps it decided a tumor was just a smudge on the lense, and invented some brain to go behind it. An operator expecting to see brain and seeing brain wont think anything of it. When the patient dies, they may look back and say "wow, that tumor didn't exist at all just 3 days before! that should be impossible!".

tldr: Having an ai that might make mistakes is one thing, having an ai that can just invent exactly the data everyone is expecting to see is dangerous.

1. Measure outside interference sources 2. Measure MRI of "nothing" 3. Use ML to estimate f(interference) = noise 4. Subtract estimated noise from signal

So the noise removal process has no awareness of brains, skeletons, etc.