TL;DR for operators Belmadani et al. study a question every serious enterprise LLM team eventually meets after the prototype stops looking magical: which adaptation bill is actually worth paying?1 In French medical question answering, they compare continual pretraining (CPT), supervised fine-tuning (SFT), and CPT followed by SFT across Gemma, Mistral, and Llama-family models, with general, instruction-tuned, and medical initializations. ...
Chest X-rays are not product reviews. This should not need saying, but much of automated report evaluation has behaved as if the difference were mostly decorative. A generated radiology report can sound fluent, mention familiar anatomy, and overlap nicely with a reference report while still missing the sentence that matters. A model that overlooks a life-threatening pneumothorax has not made the same kind of mistake as a model that fails to mention age-appropriate aortic calcification. One error can change patient management immediately. The other may be little more than reporting style. ...
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 ...