Clinical Question

Research on clinical question answering (CQA) focuses on developing AI systems, particularly large language models (LLMs), capable of reliably answering complex medical questions and providing evidence-based reasoning for clinical decision-making. Current efforts concentrate on improving the accuracy and explainability of LLMs through techniques like retrieval-augmented generation (RAG) and architectures designed to mimic human cognitive processes, addressing limitations in handling nuanced, multi-faceted clinical scenarios. The ultimate goal is to create trustworthy AI tools that augment clinical expertise, improving the efficiency and accuracy of healthcare.