With a big enough data set of [all kinds of bio values, including ones considered irrelevant for that disease] labeled with diagnoses, I suspect we could get very fast and accurate automatic diagnoses, even from a limited data set currently considered uncorrelated. Rather than going to your primary care physician, you'd go into the standardized, mass-produced and thus reasonably cheap everything-scanner, and you could likely get a more accurate diagnosis (or at least "things to check") than the average doctor would be able to give you under the practical constraints they typically operate under (time, available information/diagnostics).
This goes in that direction, and I'm really excited to see where it goes. I could imagine that given enough training data, ML models will be able to pick up on minute details that make it possible to diagnose diseases that weren't historically considered ultrasound-diagnoseable from this kind of detailed ultrasound.
I think combining it with gas chromatography/mass spectrometry of e.g. breath or blood/sweat/urine samples would also have the potential to be a cost-effective diagnosis method - lots of data, probably not all too useful for human interpretation, but would open the potential to walk up to a machine, breathe into it, spit into it, pee into it, give it a swab, and have it come up with an accurate diagnosis without invasive testing. If mass produced, the cost of something like this could easily drop below the cost of a typical doctor's visit. (I googled it and it seems like GCMS is already used for some diagnoses, but screening only for a few specific diseases rather than "throw ML at it and try to diagnose everything").