Cost is not always about size. That is an inconvenient sentence for anyone trying to sell a larger medical foundation model by waving parameter counts like a hospital procurement trophy. In ECG modeling, the expensive question is not simply whether one can pretrain on more recordings. The harder question is whether the model architecture and pretraining task actually match the structure of the signal. ...
Beam planning sounds like the sort of work automation should have solved years ago. There is a target. There are organs at risk. There are dose constraints. There is an optimizer. Surely the machine should find the best plan while humans do something more dignified than nudging parameters inside a treatment planning system for the seventeenth time. ...
TL;DR for operators This paper is best read as a workflow paper, not a miracle-doctor paper. It shows that pre-consultation AI becomes more useful when it stops behaving like a polite symptom box and starts behaving like an intake coordinator with a checklist, memory, and a sense of unfinished business. The system decomposes pre-consultation into triage, history of present illness, past history, and chief complaint generation. A Controller agent decides what still needs to be asked. A Monitor agent checks whether subtasks are complete. A Prompter and Inquirer convert those gaps into the next clinical question. This is less theatrical than “AI doctor,” which is precisely why it matters. ...
TL;DR for operators DxDirector-7B is interesting because it does not behave like a normal medical chatbot. It does not wait for a doctor to gather a neat case history and then offer a polished answer. It starts with a vague chief complaint, decides what information is missing, asks for clinical operations when necessary, and stops when it believes enough evidence exists to make a diagnosis.1 ...