Clinical QA

Clinical question answering studies how AI systems answer medical questions, from exam-style queries to patient-specific questions over electronic health records, guidelines, and research. Common approaches include fine-tuning and prompting language models, retrieval-augmented generation, structured queries and knowledge graphs, and agent workflows that search, verify, or cite evidence. Evaluation spans medical QA benchmarks, clinician-reviewed open-ended questions, and tasks involving longitudinal records, with growing attention to grounding, privacy, consistency, and safe abstention. A central challenge is that fluent or correct-looking answers may omit important evidence or contain unsupported claims, especially when questions require multi-step reasoning across sources or encounters.