Medical Question Answering
Medical Question Answering (MedQA) focuses on developing AI systems capable of accurately and reliably answering complex medical questions, improving access to information and aiding clinical decision-making. Current research emphasizes enhancing Large Language Models (LLMs) through techniques like Retrieval-Augmented Generation (RAG), knowledge graph integration, and parameter-efficient fine-tuning to mitigate hallucinations and improve factual accuracy. These advancements aim to create trustworthy and robust MedQA systems, ultimately impacting medical education, patient care, and the efficiency of healthcare professionals.
Papers
MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answering
Robert Osazuwa Ness, Katie Matton, Hayden Helm, Sheng Zhang, Junaid Bajwa, Carey E. Priebe, Eric Horvitz
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study
Martin J. Hetz, Nicolas Carl, Sarah Haggenmüller, Christoph Wies, Maurice Stephan Michel, Frederik Wessels, Titus J. Brinker