Medical Question

Research in medical question answering (MQA) focuses on developing AI systems capable of accurately answering and explaining medical questions, mirroring the reasoning of human medical professionals. Current efforts leverage large language models (LLMs), often augmented with retrieval-augmented generation (RAG) techniques and fine-tuned on specialized medical datasets, to improve accuracy and explainability. These advancements aim to create tools for clinical decision support, improving patient care and facilitating medical research by efficiently processing and summarizing vast amounts of medical literature. The field is actively addressing challenges such as handling complex clinical scenarios, mitigating hallucinations, and ensuring multilingual capabilities.