It's worth thinking of an MRI as a programmable machine for doing certain types of physics experiments.
Sometimes you have an area of interest, sometimes you don't. A lot of the practical (i.e. clinical level, not research work) on specific areas of interest is still in coil design, since body coils often don't do well.
There are all sorts of things that make it difficult (e.g. imaging is in frequency domain, localizing things with gradients can be time consuming in ways not entirely directly related to clarity, etc.)
This sort of thing is addressing issues that come up with acceleration techniques that rely on redundancy in the sampled space to "cheat" and not capture everything. The obvious concern with a ML approach here is that it may replace something interesting with something more normal.
I'd hate to be the one tasked with V&V for this, honestly.
What bothers me here is when the artifacts hide underlying pathology, and these algorithms "learn" what a normal knee mri looks like and just show you that. IMO it is a medical liability that must be addressed.
This is exactly what is done already.
Every method of one can name for reducing scan times is used, and some we can’t name are used too. Speed nearly always comes at the expense of quality, although some acceleration techniques and tech developments have lead to improvements that are pretty much without time penalty. These include signal digitisation at the coil and other methods of getting more for for less (note that this equation doesn’t include money!).