Medical dialogue systems (MDS) enhance patient-physician communication, improve healthcare accessibility, and reduce costs. However, acquiring suitable data to train these systems poses significant challenges. Privacy concerns prevent the use of real conversations, necessitating synthetic alternatives. Synthetic dialogue generation from publicly available clinical notes offers a promising solution to this issue, providing realistic data while safeguarding privacy. Our approach, SynDial, uses a single LLM iteratively with zero-shot prompting and a feedback loop to generate and refine high-quality synthetic dialogues. The feedback consists of weighted evaluation scores for similarity and extractiveness. The iterative process ensures dialogues meet predefined thresholds, achieving superior extractiveness as a result of the feedback loop. Additionally, evaluation shows that the generated dialogues excel in factuality metric compared to the baselines and has comparable diversity scores with GPT4.
Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance
Aalto University（阿尔托大学）；Tencent（腾讯）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Hong Kong Polytechnic University（香港理工大学）；Aarhus University（奥胡斯大学）；Technical University of Munich（慕尼黑工业大学）
Mapping Patient-Perceived Physician Traits from Nationwide Online Reviews with LLMs
Johns Hopkins School of Medicine（约翰霍普金斯医学院）；Johns Hopkins University（约翰霍普金斯大学）；Carey Business School, Johns Hopkins University（约翰霍普金斯大学Carey商学院）；Center for Digital Health Artificial Intelligence (CDHAI)（数字健康人工智能中心（CDHAI））
NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes
University of Massachusetts（马萨诸塞大学）；University of Minnesota（明尼苏达大学）；Microsoft（微软公司）；University of Edinburgh（爱丁堡大学）；University of Massachusetts, Lowell（马萨诸塞大学洛厄尔分校）
Comparisons between a Large Language Model-based Real-Time Compound Diagnostic Medical AI Interface and Physicians for Common Internal Medicine Cases using Simulated Patients
Shihwa Medical Center（始华医疗中心）；Helpmedoc Inc.（Helpmedoc公司）；Asan Medical Center（峨山医疗中心）；University of Ulsan College of Medicine（蔚山大学医学院）
本研究开发了基于大语言模型的实时复合诊断医疗AI界面，通过非随机临床试验与医生比较，发现其在常见内科病例中诊断准确率更高（80% vs 50-70%）、时间更短（缩短44.6%）、成本更低（降低98.1%），患者满意度相当，显示出辅助初级保健的潜力。
Dual-stage and Lightweight Patient Chart Summarization for Emergency Physicians
Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada（电气与软件工程系，卡尔加里大学）；Department of Emergency Medicine, University of Calgary, Calgary, AB, Canada（急诊医学系，卡尔加里大学）；School of Information, University of Michigan, Ann Arbor, MI, USA（信息学院，密歇根大学）；Rockview General Hospital, Calgary, AB, Canada（罗克维尔总医院）
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）
Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care
Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）；Department of Medicine, Azienda Sanitaria Universitaria Integrata del Trentino (ASUIT)（特伦托大学整合卫生机构医学部）；Center for Medical Sciences (CISMed), University of Trento（特伦托大学医学科学中心）；Universitat Politècnica de València（瓦伦西亚理工大学）；The University of Edinburgh（爱丁堡大学）
Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds
Zhejiang University（浙江大学）；Tencent Youtu Lab（腾讯优图实验室）；Jarvis Research Center（贾维斯研究中心）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
RealICU: Do LLM Agents Understand Long-Context ICU Data? A Benchmark Beyond Behavior Imitation
Technical University of Munich（慕尼黑技术大学）；TUM University Hospital（TUM大学医院）；LMU Munich（慕尼黑大学）；University of Sheffield（谢菲尔德大学）；University of Oxford（牛津大学）；Zhongshan Hospital Fudan University（复旦大学中山医院）；Sun Yat-sen University Cancer Center（中山大学肿瘤中心）；Imperial College London（伦敦帝国学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；relAI – Konrad Zuse School of Excellence in Reliable AI（relAI – 卡诺夫茨卓越可靠人工智能学校）
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）
Can "AI" Be a Doctor? A Study of Empathy, Readability, and Alignment in Clinical LLMs
Department of Electrical Engineering and Information Technology（电子工程与信息科技系）；University of Naples Federico II（那不勒斯费德里科二世大学）；Department of Translational Medical Sciences（转化医学科学系）；University of Campania ”Luigi Vanvitelli”（坎帕尼亚“路易吉·范维蒂利”大学）；Department of Computer Science, McCormick School of Engineering and Applied Science（计算机科学系，麦科马克工程与应用科学学院）；Northwestern University（西北大学）
Synthetic or Authentic? Building Mental Patient Simulators from Longitudinal Evidence
SJTU Paris Elite Institute of Technology（上海交通大学巴黎精英理工学院）；X-LANCE Lab（X-LANCE实验室）；School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算机与信息系统学院）
The Dialogue That Heals: A Comprehensive Evaluation of Doctor Agents' Inquiry Capability
Department of Computer Science and Technology, Tsinghua University, China（清华大学计算机科学与技术系）；Institute for AI Industry Research (AIR), Tsinghua University, China（清华大学人工智能产业研究院）
Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients
Division of Hospital Medicine, Department of Medicine, Stanford University（斯坦福大学医院医学部，医学部）；Stanford University School of Medicine（斯坦福大学医学院）；Stanford Health Care, Technology and Digital Solutions（斯坦福健康，技术与数字解决方案）；Division of Computational Medicine, Stanford University（斯坦福大学计算医学部）；Stanford Health Care, Nursing Informatics（斯坦福健康，护理信息学）
