I don't know those specific fields but when I was in grad school, I saw a lot of ML applied to biomedical signal processing. Firstly, I think there is a ton of

I don't know those specific fields but when I was in grad school, I saw a lot of ML applied to biomedical signal processing. Firstly, I think there is a ton of good in ML, specifically the ability to pull out (and rely upon but not reveal) patterns that a human couldn't. But what I saw as a challenge was when causal mechanisms, or the underlying physics is ignored, and ML is used to find a correlation. For meaningful application of ML to a discipline like you name, i think there should be a clear physical mechanism that underlies whatever ML is being used for, not just "we trained a model and it predicted this". Otherwise, ML (and gradient descent trained DNNs in particular) are very effective tools.