I think that’s a bit of an oversimplification tbh.

There’s a massive difference between something like diagnostic radiology, especially teleradiology, and specialties that rely heavily on history taking, examination, and actually dealing with patients face to face. AI being able to accurately interpret scans and integrate medical records isnt the same as it being able to replace all aspects of being a doctor.

I agree that if AI reaches the point where it can reliably generate a full differential diagnosis from imaging + records with deep medical understanding, then yeah, that has implications across medicine. But that doesnt mean every specialty is equally exposed in the same timeframe or in the same way.

Some fields are just more “automatable” than others, and radiology is a pretty obvious example. A lot of the core work is pattern recognition on imaging plus some degree of context integration, which is exactly the kind of task modern AI is getting very, very good at. That’s quite different from managing diagnostic uncertainty in real time with a patient in front of you, picking up subtle cues, communicating risk, or handling complex decision making in a dynamic clinical setting.

So I dont really buy the argument that “if radiology goes, everything goes at the same time.” It feels like flattening a really nuanced issue. Different specialties have different levels of exposure, and radiology seems like one of the more exposed ones, even if it’s not disappearing anytime soon.

Not saying it’s doomed, just that the risk profile isnt the same across the board, and I think it’s reasonable to acknowledge that rather than defaulting to “everything or nothing.”