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I can say that most radiologists would not want a computer trying to fix poor scan data. If the underlying data is bad, they would have recommend an orthogonal imaging abnormality. "I don't know" is a possible response radiologists can give. Trying to add training data to "clean up" an image would bias the read towards "normal".

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Spot on. When I can't interpret a study due to artifact, I say that in my report.

Let's say there's a CTA chest that is limited because the patient breathed while the scan was being acquired, I need to let the ordering clinician know that the study is not diagnostic, and recommend an alternative.

If AI eliminates the artifact by filling in expected but not actually acquired data, I am screwed and the patient is screwed.

To nitpick, wouldn't it by definition bias the read toward normal? I suppose the problem is more that you don't want to bias it to normal if it wasn't.

The training data is going to have far more normal scans of any given part than it will abnormal.

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