Medical LLMs

Medical large language models are studied for tasks such as diagnosis, clinical coding, note summarization, triage, and patient communication. Common approaches include clinical pretraining and fine-tuning, retrieval-augmented generation with guidelines or knowledge graphs, tool-using agents, and multimodal models that combine text with images or structured records. Evaluation ranges from question-answering benchmarks to realistic consultations, EHR workflows, and clinician studies, with growing attention to calibration, privacy, bias, and evidence grounding. A central challenge is that strong scores on fixed tests do not ensure reliable behavior with incomplete or changing information, misleading patient input, or high-stakes decisions, so human oversight and real-world validation remain important.