The increasing demand for mental health services has outpaced the availability of real training data to develop clinical professionals, leading to limited support for the diagnosis of depression. This shortage has motivated the development of simulated or virtual patients to assist in training and evaluation, but existing approaches often fail to generate clinically valid, natural, and diverse symptom presentations. In this work, we embrace the recent advanced language models as the backbone and propose a novel clinician-in-the-loop patient simulation pipeline, TalkDep, with access to diversified patient profiles to develop simulated patients. By conditioning the model on psychiatric diagnostic criteria, symptom severity scales, and contextual factors, our goal is to create authentic patient responses that can better support diagnostic model training and evaluation. We verify the reliability of these simulated patients with thorough assessments conducted by clinical professionals. The availability of validated simulated patients offers a scalable and adaptable resource for improving the robustness and generalisability of automatic depression diagnosis systems.
Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media
Hefei University of Technology（合肥工业大学）；University of Science and Technology of China（中国科学技术大学）；East China Normal University（华东师范大学）；Pennsylvania State University（宾夕法尼亚州立大学）
Psychologically-Grounded Graph Modeling for Interpretable Depression Detection
Singapore Institute of Technology（新加坡科技学院）；Nanyang Technological University（南洋理工大学）；ELLIS Institute Finland（芬兰ELLIS研究所）；University of Turku（图尔库大学）；Princess Nourah bint Abdulrahman University（普里西拉努拉·本·阿卜杜勒拉赫曼大学）
Optimizing Large Language Models for Detecting Symptoms of Comorbid Depression or Anxiety in Chronic Diseases: Insights from Patient Messages
Stanford University（斯坦福大学）；Stanford School of Medicine（斯坦福医学院）；Stanford Health Care（斯坦福医疗保健系统）；Google LLC（谷歌公司）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Veterans Affairs Palo Alto Health Care System（帕洛阿尔托退伍军人事务医疗保健系统）
PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality and Mental Health
Stony Brook University（石溪大学）；New York University（纽约大学）；University of Melbourne（墨尔本大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Pennsylvania（宾夕法尼亚大学）；Stanford University（斯坦福大学）
The Colorful Future of LLMs: Evaluating and Improving LLMs as Emotional Supporters for Queer Youth
Baruch Ivcher School of Psychology, Reichman University（赖希曼大学巴鲁克·伊夫切尔心理学院）；The Education Department, Ariel University（阿里尔大学教育系）；Rambam Medical Center（兰巴姆医疗中心）；Faculty of Data and Decision Sciences, Technion, IIT（以色列理工学院数据与决策科学学院）
MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences
Dartmouth College（达特茅斯学院）；Colby College（科尔比学院）；Brown University（布朗大学）；Cornell Tech（康奈尔科技学院）；Massachusetts Institute of Technology（麻省理工学院）；University College London（伦敦大学学院）；Biocogniv Inc（比奥认知公司）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；Microsoft Research（微软研究院）
PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents
Tsinghua University（清华大学）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；Medical Psychological Institute, Central South University（中南大学医学心理学研究所）；National Clinical Research Center for Mental Disorders（国家精神疾病临床研究中心）；Carnegie Mellon University（卡内基梅隆大学）
GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing
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（新加坡管理学院计算机与信息学院）；Mental Health Education Center, Northeastern University（东北大学心理健康教育中心）；School of Psychology, Northeast Normal University（东北师范大学心理学系）；Faculty of psychology, Southwest University（西南大学心理学系）；School of Sociology and Psychology, Central University of Finance and Economics（中央财经大学社会学与心理学学院）；College of Arts, Northeastern University（东北大学艺术学院）
