[pooper]

I remember talking to a radiologist who said he was sure something like this was coming like ten years ago where instead of a radiologist looking at scans manually, a machine would go through a lot of images and flag some for manual review.

We haven't even gotten there yet, have we?

[osrec]

Yes, we absolutely are there: https://youtu.be/D3oRN5JNMWs?feature=shared

My professor (Sir Michael Brady) at university 14 years ago set up a company to do this very thing, and he already had reliable models back before 2010. I believe their company was called Oxford Imaging or something similar.

[wruza]

Yep, everyone seems to forget that ML was available before 2021. Had a conversation recently with my former colleague who learned about some plastic packaging company which used "AI" to predict client orders and inform them about scheduling implications. When I told him that you don't need Transformers and 30GB models for that, he was quasi-confused, cause he kinda knew it but the hype just overtook his knowledge.

[anon373839]

In ML courses, you’re taught to try simpler methods and models before turning to more complex ones. I think that’s something that hasn’t made it into the mainstream yet.

A lot of people seem to be using GPT-4 for tasks like text classification and NER, and they’d be much better off fine-tuning a BERT model instead. In vision, too, transformers are great but a lot of times, a CNN is all you really need.

[dagw]

We haven't even gotten there yet, have we?

Yes and no. Countless teams have solved exactly this problems at universities and research groups across the world. Technically it's pretty much a solved problem. The hard part is getting the systems out of the labs and certified as an actual product and convincing hospitals and doctors to actually use them.

[matheusd]

Maybe it's a liability issue, not a competency issue.