I actually think ML models would excel here. Humans are famously bad at estimating and weighing risks and there's really only so much data a single human brain can store and draw conclusions from. Not to mention bias like female patients being chronically under-diagnosed by male doctors.

If you fed a mountain of surgery outcome data into an ML model, I imagine it'd be shockingly effective and (hopefully) less biased on sex and race.

It'd probably be helpful for initial diagnosis, but I'm less confident in that. Postop risk assessment is mostly straight statistics, and statistical inference is what ML models do. Diagnosis is a bit more subjective and complex, though it is in the same general domain.

The real trick is going to be conditioning doctors to not blindly trust the risk assessment model. Though I would hope that it'd be accurate enough for that anyway

The article mentions a previously existing risk scoring system, which was presumably already trying to deal with the problem of humans not being great at evaluating the risk.