There were similar discussions a few year ago when deep learning was not commonly used yet and compressed sensing was the hot topic of the moment. It can reconstruct MRI or CT images from limited data (and thus allows for quick MR scans or low dose CT) but you have to satisfy a sparsity condition that is seldom granted. There are a few use cases (like MR angiography) where the data is sparse enough and compressed sensing works great.
For deep learning techniques, you need to be very cautious about which structures your network may remove or introduce.
I disagree. I think using techniques that work by attempting to model physical processes that we understand are philosophically different from ML approaches that are learning arbitrary functions.
Importantly, the internals of non-machine-learning based approaches are more readily understandable and their output is much more predictable.
If you need an MRI or a CT of an area adjacent to orthopedic implants, you are currently 100% SOL because distortion or reflection artifacts from the metal completely destroy the imagery across a medically significant distance. There are computational filtering techniques for reducing these artifacts, but, respectfully, they are still really terrible, and close to the implants you can't see shit. All advancements in this area short of inventing new imaging physics will most likely be purely computational corrections. Consider that.