Greed. Case study: insulin.

I think that’s just pattern matching like facial recognition. It covers more imaging in less time and can help identify areas of concern. But that doesn’t need trillions of dollars.

I work in biomed R&D, and specifically spent several years in Radiology.

Industry consensus is that CAD occasionally picks up anomalies that a radiologist would have missed, but the false positives it picks up are noisy enough to largely offset that benefit. It’s fine if used as a second pass to catch areas a human missed, but doesn’t actually perform “better than a doctor” in a vacuum, precisely because it’s not thinking for itself and e.g. cross referencing the imaging against clinical history.

A deep learning model can tell biological sex from retina pictures, but not even the best eye doctors can. You feed it a pile of images labeled "these are from men" and "these are from women," and it figures out the differences and applies that knowledge to pictures it's never seen before. As far as I know, we still don't know what exactly it's picking up on - or if it's even something a human could distinguish - but for an AI it's not a problem.

I think the term "AI" has just been a bit stained by all the people conflating it with GenAI. Yes, GenAI is AI, but the term AI covers all kinds of systems, and GenAI is just one subcategory.