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
PMC-LLaMA: Towards Building Open-source Language Models for Medicine
Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, Weidi Xie
ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development
Ta Duc Huy, Nguyen Anh Tu, Tran Hoang Vu, Nguyen Phuc Minh, Nguyen Phan, Trung H. Bui, Steven Q. H. Truong