Medical Question Answering Benchmark

Medical question answering (MQA) benchmarks are crucial for evaluating the performance of large language models (LLMs) in healthcare, focusing on accuracy, reasoning, and explainability within the medical domain. Current research emphasizes developing comprehensive benchmarks with diverse question types and multiple explanations, often incorporating retrieval-augmented generation (RAG) and graph-based methods to improve accuracy and reliability, and exploring smaller, more computationally efficient models for wider accessibility. These advancements are vital for building trustworthy and clinically useful AI systems, ultimately improving patient care and medical research.