Clinical Question Answering (QA) systems enable doctors to quickly access patient information from electronic health records (EHRs). However, training these systems requires significant annotated data, which is limited due to the expertise needed and the privacy concerns associated with clinical data. This paper explores generating Clinical QA data using large language models (LLMs) in a zero-shot setting. We find that naive prompting often results in easy questions that do not reflect the complexity of clinical scenarios. To address this, we propose two prompting strategies: 1) instructing the model to generate questions that do not overlap with the input context, and 2) summarizing the input record using a predefined schema to scaffold question generation. Experiments on two Clinical QA datasets demonstrate that our method generates more challenging questions, significantly improving fine-tuning performance over baselines. We compare synthetic and gold data and find a gap between their training efficacy resulting from the quality of synthetically generated answers.
De-identification is not enough: a comparison between de-identified and synthetic clinical notes
Department of Computer Science, University of Manitoba（曼尼托巴大学计算机科学系）；McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦克威廉姆斯生物医学信息学院）
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）
Detecting the Clinical Features of Difficult-to-Treat Depression using Synthetic Data from Large Language Models
University of Oxford（牛津大学）；University of Liverpool（利物浦大学）；Mersey Care NHS Foundation Trust（默西护理国民保健信托基金会）；Oxford Health NHS Foundation Trust（牛津健康国民保健信托基金会）；NIHR Oxford Health Biomedical Research Centre（NIHR牛津健康生物医学研究中心）
Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text
Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校信息与计算机科学学院）；U.S. Department of Veterans Affairs（美国退伍军人事务部）；Department of Medicine, University of Massachusetts Chan Medical School（马萨诸塞大学查恩医学院医学部）；Miner School of Computer and Information Sciences, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校米纳尔计算机与信息科学学院）
Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation
Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（巴黎- Rocquencourt 国家信息与自动化研究所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒实验室）；University of Chicago（芝加哥大学）；Stanford University（斯坦福大学）；Purdue University（普渡大学）；University of Southern California（南加州大学）
Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale
Australian e-Health Research Centre, CSIRO, Australia（澳大利亚电子健康研究中心，CSIRO，澳大利亚）；National Library of Medicine, National Institutes of Health, USA（国家医学图书馆，国立卫生研究院，美国）
A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment
Microsoft Healthcare & Life Sciences（微软医疗与生命科学）；Microsoft Research Montréal（微软研究院蒙特利尔）；Mila, Université de Montréal（蒙特利尔大学米拉研究所）；IKIM, University Hospital Essen（埃森大学医院人工智能医学研究所）；Cancer Research Center Cologne Essen（科隆埃森癌症研究中心）；German Cancer Consortium（德国癌症联盟）；TU Dortmund（多特蒙德工业大学）
UKP Lab, Department of Computer Science and Hessian Center for AI (hessian.AI), Technische Universität Darmstadt（德国达姆施塔特技术大学UKP实验室、计算机科学系和海森人工智能中心（hessian.AI））；Zuse School ELIZA（Zuse学院ELIZA）；National Research Center for Applied Cybersecurity ATHENE（应用网络安全国家研究中心ATHENE）；Indian Institute of Technology Delhi（印度德里理工学院）；Yardi School of Artificial Intelligence（Yardi人工智能学院）；University of Louisville（路易斯维尔大学）；University of Toledo（托莱多大学）
Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes
IT-Infrastructure for Translational Medical Research（转化医学研究信息基础设施）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Technische Universität Berlin（柏林技术大学）；German Research Center for Artificial Intelligence (DFKI), Berlin（德国人工智能研究中心（DFKI），柏林）；IKIM, Charité - Universitätsmedizin Berlin（IKIM，柏林夏里特大学医学中心）
Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data
Mass General Brigham, Harvard Medical School（哈佛医学院麻省总医院布里格姆医疗系统）；Massachusetts Institute of Technology（麻省理工学院）；University of Wisconsin School of Medicine and Public Health（威斯康星大学医学与公共卫生学院）；Loyola University Chicago（芝加哥洛约拉大学）
DermaSynth: Rich Synthetic Image-Text Pairs Using Open Access Dermatology Datasets
Imperial College London（帝国理工学院）；Yildiz Technical University（伊尔德兹技术大学）；Usak Research and Training Hospital（乌萨克研究与培训医院）；Istanbul Medeniyet University（伊斯坦布尔梅德尼耶特大学）；Universität der Bundeswehr München（慕尼黑联邦国防军大学）；Istanbul Medicana Atakoy Hospital（伊斯坦布尔 Medicana Atakoy 医院）
Clinical Document Corpora -- Real Ones, Translated and Synthetic Substitutes, and Assorted Domain Proxies: A Survey of Diversity in Corpus Design, with Focus on German Text Data
Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig（莱比锡大学医学信息学、统计学与流行病学研究所）
Cross-Institutional Dental EHR Entity Extraction via Generative AI and Synthetic Notes
The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；University of California San Francisco（加利福尼亚大学旧金山分校）；Willamette Dental Group（威拉米特牙科集团）；Skourtes Institute（斯库特斯研究所）
From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity
X - LANCE Lab, Shanghai Jiao Tong University, China（上海交通大学X-LANCE实验室）；Shanghai Mental Health Center, SJTU School of Medicine, China（上海精神卫生中心，上海交通大学医学院）；Chen Frontier Lab for AI and Mental Health, Tianqiao and Chrissy Chen Institute, Shanghai, China（陈前沿人工智能与心理健康实验室，田青和克里斯蒂娜·陈研究所）
How Real Are Synthetic Therapy Conversations? Evaluating Fidelity in Prolonged Exposure Dialogues
College of Information Sciences and Technology, Penn State University（宾夕法尼亚州立大学信息科学与技术学院）；Department of Psychiatry and Behavioral Sciences, Emory University（埃默里大学精神病学与行为科学系）；School of Interactive Computing, Georgia Tech（佐治亚理工学院交互计算学院）；School of Psychology, Georgia Tech（佐治亚理工学院心理学学院）
Explainable AI for Screening Abuse-Related Trauma in Bangladeshi Children: A Training-Free Multimodal Framework Evaluated on Noise-Aware Synthetic Data
One-Stop Crisis Centres (OCC)（一站式危机中心（OCC））；Department of Social Services (DSS)（社会服务部（DSS））
