Medical conversations offer insights into clinical communication often absent from Electronic Health Records. However, developing reliable clinical Natural Language Processing (NLP) models is hampered by the scarcity of domain-specific datasets, as clinical data are typically inaccessible due to privacy and ethical constraints. To address these challenges, we present a pipeline for generating synthetic Dutch medical dialogues using a Dutch fine-tuned Large Language Model, with real medical conversations serving as linguistic and structural reference. The generated dialogues were evaluated through quantitative metrics and qualitative review by native speakers and medical practitioners. Quantitative analysis revealed strong lexical variety and overly regular turn-taking, suggesting scripted rather than natural conversation flow. Qualitative review produced slightly below-average scores, with raters noting issues in domain specificity and natural expression. The limited correlation between quantitative and qualitative results highlights that numerical metrics alone cannot fully capture linguistic quality. Our findings demonstrate that generating synthetic Dutch medical dialogues is feasible but requires domain knowledge and carefully structured prompting to balance naturalness and structure in conversation. This work provides a foundation for expanding Dutch clinical NLP resources through ethically generated synthetic data.
Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学研究所）；Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹大学医学中心）
We investigate different natural language processing (NLP) approaches based on contextualised word representations for the problem of early prediction of lung cancer using free-text patient medical notes of Dutch primary care physicians. Because lung cancer has a low prevalence in primary care, we also address the problem of classification under highly imbalanced classes. Specifically, we use large Transformer-based pretrained language models (PLMs) and investigate: 1) how \textit{soft prompt-tuning} -- an NLP technique used to adapt PLMs using small amounts of training data -- compares to sta…
GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations
School of Computing Science, University of Glasgow, UK（格拉斯哥大学计算机科学学院）；School of Natural and Computing Science, University of Aberdeen, UK（阿伯丁大学自然与计算科学学院）
JMedEthicBench: A Multi-Turn Conversational Benchmark for Evaluating Medical Safety in Japanese Large Language Models
Kyoto University（京都大学）；Hohai University（河海大学）；The University of Tokyo（东京大学）；University of Science and Technology of China（中国科学技术大学）；Hong Kong Polytechnic University（香港理工大学）
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）
Real-time Speech Summarization for Medical Conversations
University of Toronto（多伦多大学）；University Health Network（大学健康网络）；College of William and Mary（威廉玛丽学院）；University of Cincinnati（辛辛那提大学）；Indiana State University（印第安纳州立大学）；FPT Software AI Center（FPT软件人工智能中心）
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation
University of Florida（佛罗里达大学）；National Institute of Technology Calicut（卡利卡特国家理工学院）；SRM, Chennai（SRM大学（金奈））；University of Cincinnati（辛辛那提大学）；Dartmouth College（达特茅斯学院）；Arizona State University（亚利桑那州立大学）
Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital
Department of Health Information Office, Data & Digitalization, University Medical Center（健康信息办公室、数据与数字化化大学医院）；Department of Trauma Surgery / Orthopedics, University Medical Center（创伤外科/骨科大学医院）；Department of Orthopaedic Surgery, Massachusetts General Hospital（骨科手术科马萨诸塞总医院）；Harvard Medical School Orthopaedic Trauma Initiative（哈佛医学院骨创伤倡议）；Department of Orthopaedic Surgery and Sports Medicine, Amsterdam UMC location University of Amsterdam（骨科手术与运动医学部门，阿姆斯特丹UMC地点大学）；Department of Orthopaedic Trauma, Flinders University Medical Centre（骨创伤部门，弗林德斯大学医疗中心）；Department of Otolaryngology – Head and Neck Surgery, University Medical Center（耳鼻喉科-头颈外科大学医院）；Department of Pediatrics, Pediatrics Nephrology, Beatrix Children’s Hospital, University Medical Center（儿科、儿科肾病学，比西特儿童医院，大学医院）
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a deeper understanding of conversational context, and generate a wide variety of responses. This flexibility comes at the cost of control, a concerning tradeoff in doctor/patient interactions. Inaccuracies, typos, or undesirable content in the training data will be reproduced by the model at inference time. We trade a small amount of labeling effort and some loss of response variety in exchange for quality control. Mo…
Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond
Intron（英创公司）；BioRAMP（BioRAMP机构）；Brown University（布朗大学）；Indian Institute of Information Technology Allahabad（阿拉哈巴德印度信息技术学院）；University of Minnesota-Twin Cities（明尼苏达大学双城分校）；University of Glasgow（格拉斯哥大学）；University of Florida（佛罗里达大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Georgia Institute of Technology（佐治亚理工学院）
该工作提出 Afrispeech-Dialog 基准数据集，通过 50 段非洲口音英语对话评估 ASR 和说话人分离系统，发现口音导致性能下降超 10%，并揭示 ASR 错误对医疗摘要的影响，呼吁构建更具包容性的低资源语音数据集。
OncoGPT: A Medical Conversational Model Tailored with Oncology Domain Expertise on a Large Language Model Meta-AI (LLaMA)
Shenzhen Kanghua Juntai Biotech Co. Ltd.（深圳康华君泰生物科技有限公司）；University of California, Davis（加利福尼亚大学戴维斯分校）；The First Affiliated Hospital of Jinan University（暨南大学附属第一医院）
RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；School of Computing, Clemson University（克莱姆森大学计算机学院）；Washington University School of Medicine in St. Louis（圣路易斯华盛顿大学医学院）；The Keck School of Medicine of the University of Southern California（南加州大学凯克医学院）；Mayo Clinic（梅奥诊所）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）
Context-Aware Classification and Grading of Sensitive Information in Online Conversational Health Data
School of Computing, Macquarie University, Australia（麦考瑞大学计算机学院）；Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；School of Mathematics and Statistics, Shandong University of Technology, China（山东理工大学数学与统计学院）；Digital Intelligence Center, Fuzhou University Affiliated Provincial Hospital, China（福州市附属省医院数字智能中心）；School of Computer Science and Engineering, UNSW, Australia（新南威尔士大学计算机科学与工程学院）
Nedap Healthcare（内达普医疗）；University of Twente（特文特大学）；Radboud University（拉德堡德大学）
Unstructured information in electronic health records provide an invaluable resource for medical research. To protect the confidentiality of patients and to conform to privacy regulations, de-identification methods automatically remove personally identifying information from these medical records. However, due to the unavailability of labeled data, most existing research is constrained to English medical text and little is known about the generalizability of de-identification methods across languages and domains. In this study, we construct a varied dataset consisting of the medical records of…
Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias
Singapore General Hospital（新加坡中央医院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Center（新加坡国家眼科中心）；Byers Eye Institute, Stanford University（斯坦福大学拜尔斯眼科研究所）；National University of Singapore（新加坡国立大学）
DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation
School of Data Science, Fudan University（复旦大学数据科学学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；University of Toronto（多伦多大学）；School of Computer Science, Fudan University（复旦大学计算机学院）；Research Institute of Automatic and Complex Systems, Fudan University（复旦大学自动与复杂系统研究院）
MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Pattern Recognition Center, WeChat AI, Tencent Inc（腾讯公司微信人工智能模式识别中心）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Leveraging Large Language Model as Simulated Patients for Clinical Education
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；School of Computer Science, Wuhan University（武汉大学计算机学院）；CureFun Co.（CureFun公司）
Topics as Proxies for Sociodemographics: How Conversational Context Affects LLM Answers
Institute for Logic, Language and Computation, University of Amsterdam（逻辑、语言与计算研究所，阿姆斯特丹大学）；Khoury College of Computer Sciences, Northeastern University（计算机科学学院，东北大学）；Center for Language and Cognition, University of Groningen（语言与认知中心，格罗宁根大学）
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）
VeriSim: A Configurable Framework for Stress-Testing Medical AI Under Patient Communication Noise
George Mason University（乔治梅森大学）；Department of Computer Science, George Mason University（乔治梅森大学计算机科学系）；Department of Health Administration and Policy, College of Public Health, George Mason University（乔治梅森大学公共卫生学院卫生管理与政策系）；Department of Science, George Mason University（乔治梅森大学科学系）
