But these are philosophical questions, not scientific questions. "Could God be hidden in black holes" is unknowable in the same way that "Is God a flying spaghetti monster?" is unknowable. It's not an interesting scientific question because it has nothing to do with the scientific problem of how black holes work, but with the philosophical problem of whether there is a God.

And just because engineers don't usually understand what their AI models do 'under the hood' doesn't mean they can't be understood. They are fundamentally just very complex decision trees and you could in principle see why each decision in a model was made in a certain way. It'd just take a very long time.

For anatomical imaging where you want to estimate the size or growth of a structure of known shape it's reasonably common to perform simple interpolation between slices. There's also a lot of research being done regarding superresolution imaging and compressed sensing, and there are commercially available sequences for both.

It's also important to keep in mind that magnetic resonance imaging isn't acquired in the same three-dimensional space we're used to, but in k-space, which is the Fourier transform of the image measured. It's very common to acquire only, say, the first 70% of the lines of a k-space grid and then either mirror the first 30% of lines (since k-space is largely symmetrical and has the most important pixels concentrated in its center) or to zero-fill a border around the acquired image. Underampling is typically performed in this way rather than skipping entire slices.

For standard anatomical MRI in humans the resolution problem is largely 'solved' since 3D pulse sequences at good resolutions don't take that much time. For diffusion imaging with high angular resolution and fMRI it's a different question, and many undersampling methods are often combined. Parallel imaging, k-space undersampling, slice interpolation....With diffusion imaging there's also the possibility to interpolate in the 'angular domain'.

So to answer your question briefly: there is continuous research being done to shorten the duration of MRI sequences, but understanding said research requires a lot of domain knowledge since, unlike optical imaging, MRI images are acquired in the frequency domain, and often have one or more other dimensions (angular, relaxation time, diffusion coefficient etc).