Evidence-based medicine (EBM) is central to high-quality care, but remains difficult to implement in fast-paced primary care settings. Physicians face short consultations, increasing patient loads, and lengthy guideline documents that are impractical to consult in real time. To address this gap, we investigate the feasibility of using large language models (LLMs) as ambient assistants that surface targeted, evidence-based questions during physician-patient encounters. Our study focuses on question generation rather than question answering, with the aim of scaffolding physician reasoning and integrating guideline-based practice into brief consultations. We implemented two prompting strategies, a zero-shot baseline and a multi-stage reasoning variant, using Gemini 2.5 as the backbone model. We evaluated on a benchmark of 80 de-identified transcripts from real clinical encounters, with six experienced physicians contributing over 90 hours of structured review. Results indicate that while general-purpose LLMs are not yet fully reliable, they can produce clinically meaningful and guideline-relevant questions, suggesting significant potential to reduce cognitive burden and make EBM more actionable at the point of care.
EmplifAI: a Fine-grained Dataset for Japanese Empathetic Medical Dialogues in 28 Emotion Labels
Kyoto Institute of Technology, Japan（京都技术大学）；National Institute of Information and Communications Technology (NICT), Japan（信息通信技术国家研究所）；Kyoto University, Japan（京都大学）；Nara Institute of Science and Technology, Japan（奈良科学技术大学）
EMRModel: A Large Language Model for Extracting Medical Consultation Dialogues into Structured Medical Records
Taihe County People’s Hospital（太和县人民医院）；Innovation + Research Institute（创新+研究院）；GuoChuang Cloud Technology Ltd（国创云科技有限公司）；Xunfei Healthcare Technology Ltd（讯飞医疗科技有限公司）；University of Southern California（南加州大学）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）
Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges
Shanghai AI Lab（上海人工智能实验室）；Beihang University（北京航空航天大学）；Beijing Academy of AI（北京人工智能研究院）；Zhijiang Lab（之江实验室）；The Chinese University of Hong Kong（香港中文大学）；Beijing Institute of Technology（北京理工大学）；East China University of Science and Technology（华东理工大学）
Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations
UNC at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Science and Technology of China（中国科学技术大学）；George Mason University（乔治·马歇尔大学）；Yale University（耶鲁大学）；City University of Hong Kong（香港城市大学）；Rice University（得克萨斯大学奥斯汀分校）
Exploring LLM-based Data Annotation Strategies for Medical Dialogue Preference Alignment
School of Computer Science, Peking University（北京大学计算机学院）；Key Laboratory of High Confidence Software Technologies(PKU), MOE, China（教育部高可信软件技术重点实验室（北京大学））；Beijing Jiaotong University（北京交通大学）
BP4ER: Bootstrap Prompting for Explicit Reasoning in Medical Dialogue Generation
Macau University of Science and Technology（澳门科技大学）；Paradigm Inc.（第四范式公司）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Zhongkai University of Agriculture and Engineering（仲恺农业工程学院）
Benchmarking Clinical Decision Pathway Adherence in Large Language Models
Tongji University（同济大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Tongji University School of Medicine（同济大学医学院）；Sun Yat-sen University（中山大学）；Fuzhou University（福州大学）；Shenzhen Loop Area Institute（深圳河套学院）
Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards
Korea University（韩国大学）；ETH Zürich（苏黎世联邦理工学院）；Yale University（耶鲁大学）；AIGEN Sciences（AIGEN公司）；Hanyang University College of Medicine（翰雅大学医学院）；University of Ulsan College of Medicine（釜山大学医学院）
Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding
Vector Institute for Artificial Intelligence（矢量人工智能研究所）；University of Toronto（多伦多大学）；Peter Munk Cardiac Centre, University Health Network（大学健康网络彼得·蒙克心脏中心）；McGill University（麦吉尔大学）；University Health Network（大学健康网络）
Integrating Physician Diagnostic Logic into Large Language Models: Preference Learning from Process Feedback
School of Computer Science, Peking University（北京大学计算机学院）；Key Laboratory of High Confidence Software Technologies(PKU), MOE, China（教育部高可信软件技术重点实验室）；Beijing Jiaotong University（北京交通大学）
PACT: Learning Diverse Diagnostic Strategies via Privileged Synthesis and Branch Consensus
Beihang University（北京航空航天大学）；Baidu（百度）；ByteDance（字节跳动）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）；Renmin University of China（中国人民大学）
InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China（香港理工大学计算机系）；Department of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）
MedDoc-Bot: A Chat Tool for Comparative Analysis of Large Language Models in the Context of the Pediatric Hypertension Guideline
Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School（布伦瑞克工业大学与汉诺威医学院Peter L. Reichertz医学信息学研究所）；Hannover Medical School（汉诺威医学院）
Structured Dialogue System for Mental Health: An LLM Chatbot Leveraging the PM+ Guidelines
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；East China Normal University（华东师范大学）；Wuhan Research Institute of Posts and Telecommunications（武汉邮电科学研究院）
Medical Dialogue Generation via Dual Flow Modeling
The Hong Kong Polytechnic University（香港理工大学）；Southern University of Science and Technology（南方科技大学）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）
