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
A Benchmark for Long-Form Medical Question Answering
Pedram Hosseini, Jessica M. Sin, Bing Ren, Bryceton G. Thomas, Elnaz Nouri, Ali Farahanchi, Saeed Hassanpour
Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering
Nghia Trung Ngo, Chien Van Nguyen, Franck Dernoncourt, Thien Huu Nguyen