Medical QA

Medical question answering studies how AI systems answer exam-style questions, patient inquiries, clinical queries, and multi-turn conversations, sometimes using records, images, or audio. Common approaches include retrieval-augmented generation, medical-domain fine-tuning, chain-of-thought and agent-based reasoning, and reinforcement learning with clinical rubrics. Evaluation uses multiple-choice and open-ended benchmarks such as MedQA, PubMedQA, and patient-query datasets, increasingly testing grounding, safety, calibration, multilingual ability, and performance across modalities. A central challenge is that gains on exams or automatic metrics do not guarantee accurate, unbiased, well-calibrated answers in real clinical settings, where uncertainty, missing context, and harmful errors matter.