I believe at most, coding, scripting, and text processing.

Exactly, there’s a few specific use cases that the current generative AI is good at. Reading medical imagingand medication development are things I’d add to your list.

However, outside of those specific tasks AI is not particularly useful. An incremental improvement in productivity in some cases.

Medical imaging and medication development would be bespoke models no? Not standard LLMs?

Probably. I mis-spoke.

You can add transcription to your list though, it’s saving providers a lot of time on charting

Generally speaking, yes, but standard LLMs now have their own built-in image recognition so we might not be too far off from LLMs being able to describe to the doctor what they should look at on an image that’s been flagged. Take a capable LLM of a few hundred billion parameters, train it on tons of medical images and such, and it might actually be helpful. Or it might not, no way to find out until tested.

AI is not just LLMs. Much of AI is machine control, and bioinformatics.llms are the foam on top of the ocean.

Yeah, but the current hype is around LLM, the other kinda of AI are the same as they were before this started.

Alphafold almost certainly benefited from genAI research, since 2 and 3 are somewhat genAI inspired architectures, they use their own versions of transformers and also diffusion

I’m aware, and I wish people were more clear about the difference, but its LLMs that are driving these insane buildouts, not the other disciplines. This article is clearly talking about the LLM “AI”, not bioinformatics or anything that may actually be fruitful.

The LLM companies like to claim they’ll solve cancer, but its all snake oil hedged on a mythical AGI popping up and fixing everything.