Medical LLMs: Don't Prioritize Intelligence First

I've been looking at SenseTime Medical's "Dayi" medical health large model recently. The real value of this kind of thing lies in whether it can integrate into hospital workflows and pull doctors and departments out of waiting, verifying, and supplementing materials.

Its public claims are over 30 billion tokens of medical knowledge, covering more than twenty specific scenarios, and it has passed the CAC (Cyberspace Administration of China) algorithm filing. The base model has substance, productization has direction, and procurement skips one compliance explanation step. This direction is worth investing in.

But I care more about evidence of implementation. I had my team test it with a de-identified list of departmental quality control issues to see if it could clearly state what needs to be supplemented, where the basis is, and who should confirm. The results weren't stunning, but there was a nice detail: it converges answers onto process actions rather than stopping at a pretty explanation.

The biggest fear for medical LLMs is spitting out something that looks correct in the consultation room, forcing doctors to verify, nurses to follow up, and responsibility ultimately falling on the person signing off. Hospital intelligent centers can't just slap a chat box on top; the key is whether it can connect to HIS (Hospital Information Systems), electronic medical records, and quality control, and whether it leaves traceable call logs.

ROI shouldn't just be calculated by benchmark scores; it must account for how much waiting, reconciliation, and bickering are reduced. If it doesn't plug into the chain of responsibility, no matter how smart it is, it's a risk. If it does plug in, it saves the doctor's most expensive hour.