Large Language Models (LLMs) have demonstrated significant potential in medical Question Answering (QA), yet they remain prone to hallucinations and ungrounded reasoning, limiting their reliability in high-stakes clinical scenarios. While Retrieval-Augmented Generation (RAG) mitigates these issues by incorporating external knowledge, conventional single-shot retrieval often fails to resolve complex biomedical queries requiring multi-step inference. To address this, we propose Self-MedRAG, a self-reflective hybrid framework designed to mimic the iterative hypothesis-verification process of clinical reasoning. Self-MedRAG integrates a hybrid retrieval strategy, combining sparse (BM25) and dense (Contriever) retrievers via Reciprocal Rank Fusion (RRF) to maximize evidence coverage. It employs a generator to produce answers with supporting rationales, which are then assessed by a lightweight self-reflection module using Natural Language Inference (NLI) or LLM-based verification. If the rationale lacks sufficient evidentiary support, the system autonomously reformulates the query and iterates to refine the context. We evaluated Self-MedRAG on the MedQA and PubMedQA benchmarks. The results demonstrate that our hybrid retrieval approach significantly outperforms single-retriever baselines. Furthermore, the inclusion of the self-reflective loop yielded substantial gains, increasing accuracy on MedQA from 80.00% to 83.33% and on PubMedQA from 69.10% to 79.82%. These findings confirm that integrating hybrid retrieval with iterative, evidence-based self-reflection effectively reduces unsupported claims and enhances the clinical reliability of LLM-based systems.
Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions
University of Virginia（弗吉尼亚大学）；National Library of Medicine（美国国家医学图书馆）；National Institutes of Health（美国国立卫生研究院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering
New York University, New York, USA（纽约大学）；Tsinghua University, Beijing, China（清华大学）；Independent Researcher（独立研究者）；Chinese Academy of Sciences, Beijing, China（中国科学院）
MARA: A Multimodal Adaptive Retrieval-Augmented Framework for Document Question Answering
Key Laboratory of Target Cognition and Application Technology (TCAT), AIRI, CAS（目标认知与应用技术重点实验室（TCAT），空气动力研究所，中国科学院）；School of Electronic, Electrical and Communication Engineering, UCAS（电子、电气与通信工程学院，中国科学院大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（航天信息研究所，中国科学院）；Baidu Inc.（百度公司）
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs
Massachusetts Institute of Technology（麻省理工学院）；University of Michigan - Ann Arbor（密歇根大学安娜堡分校）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Harvard University（哈佛大学）；University of the Philippines（菲律宾大学）；Stevens Institute of Technology（史蒂文斯理工学院）
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；Zhongguancun Academy（中关村学院）；Shenzhen Loop Area Institute（深圳河套学院）
MedAgentBoard: Benchmarking Multi-Agent Collaboration with Conventional Methods for Diverse Medical Tasks
National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）；Centre for Medical Informatics, The University of Edinburgh（爱丁堡大学医学信息学中心）；ETH Zurich（苏黎世联邦理工学院）；Health Data Research UK（英国健康数据研究所）；Key Laboratory of High Confidence Software Technologies, Ministry of Education（教育部高可信软件技术重点实验室）
Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering
University of Electronic Science and Technology of China（电子科技大学）；Tsinghua University（清华大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Xiaomi Inc.（小米公司）；MiLM Plus
