[hfbff]

Thanks for sharing. My belief is that we need to figure out a way to make humans interact with prediction models in a virtuous way. Prediction models suck at "connecting the dots" or considering multiple sources of information (for example: multiple models predicting different outcomes). Until we get true general artificial intelligence, I think the way to go forward is to try to quantify those unknowns through confidence intervals (conformal prediction seems to be quite nice for many models) plus some multiple hypothesis testing to handle the multiple outcomes / multiple models.

This needs to be then implemented on a real flow where humans and prediction models interact (for example: approve these things automatically, send these other test for humans to revise)

[steveBK123]

Personally knowing the hit rate of these ML models & their non-explanatory nature, weighed against their low cost.. I'd argue they should be used as a default automated second opinion to radiologist opinion.

Recently went through a pet cancer death so though medical imaging, diagnostic testing, specialist escalation and second opinion workflows are pretty fresh in my mind. There is a shortage of specialists, backlog for appointments and many astonishingly bad practitioners out there.

[hef19898]

In my experience, multiple knee MRTs, the liver, the intestine, the ankle..., radiologists by default are the second opinion to the specialist, e.g. an oncologist, that sent you to get the scan in the first place. I never ever had a radiologist come up with a diagnosis by himself.