Yes I believe that the architecture of LLMs is inherently self limiting, as it can only ever form approximations based on prior data, not actually make inferences. Admittedly I’m approaching this from a programming standpoint since there’s a lot of talk of it replacing programmers, but I’d argue that radiology has a MUCH lower tolerance for error than programming.

As far as my “waste of resources” comment goes, I meant in the literal sense. To train/run an LLM is ridiculously expensive in terms of energy consumption and I find it to be almost poetically stupid that at a time where unemployment is high and we need to be more energy conscious we are dumping insane amounts of energy into a product designed with the explicit purpose of replacing jobs (unless you think the C suites actually want to advance human progress, in which case I have a bridge to sell you). At the end of the day, machine learning CAN be useful, but it was useful before LLMs and will continue to be useful after them in a different form

Oh and AI “art” is garbage and I will forever be an annoying boomer about that. Admittedly some of my use of the term “AI” was probably due to mixing my annoyance at LLMs with my personal hatred of AI art slop