I think this is a dangerous research direction under-regulated by the FDA. In order to get this sort of thing approved you just have to prove it doesn’t affect the diagnosis of a small set of abnormalities. The power of these models is enormous. They could potentially recognize and “smooth” out only certain abnormalities and there is no real way to guarantee that they won’t do that without testing it on all abnormalities.
I just spent 20% of my time at RSNA arguing with people doing similar things and everyone seems to be happy to jump over the FDA’s existing bar for reconstruction algorithms. However previous reconstruction algorithms weren’t universal function approximators with the potential to exhibit abnormality-specific behavior.
We know very well these models have the capacity to recognize certain abnormalities or learn to model the normal state of anatomy. There is also the danger of the fact that deep learning powered reconstruction will not work alongside a radiologist like other AI for medical imaging applications such as nodule detection. This means we won’t find the problem with FDA’s low regulatory bar until patients start dying.
