Medical Question Answering System

Medical Question Answering (QA) systems aim to provide accurate and reliable answers to medical questions, leveraging advancements in natural language processing to improve healthcare access and efficiency. Current research focuses on enhancing model performance through techniques like incorporating specialized medical datasets, employing machine reading comprehension (MRC) and large language models (LLMs) with retrieval-augmented generation (RAG), and developing methods for robust uncertainty estimation. These improvements are crucial for addressing challenges such as complex medical terminology and the need for reliable, trustworthy answers, ultimately impacting clinical decision-making, medical research, and patient care.