As Large Language Models increasingly power role-playing applications, simulating patients has become a valuable tool for training counselors and scaling therapeutic assessment. However, prior work remains fragmented: existing approaches rely on incompatible, non-standardized profiles, prompts, and evaluation metrics, hindering reproducibility, fair comparison, and reuse. We introduce PatientHub, a unified and modular framework that standardizes the creation, simulation, and evaluation of LLM-based patients. Our framework provides 16 patient simulators, a graph-based orchestrator for multi-turn, multi-session interactions, and a configurable LLM-as-a-judge evaluator that supports multiple rubric types. Via our command-line interface, users can generate patient profiles, run simulations, and apply rubric-driven evaluation at the turn and session level. To demonstrate PatientHub's utility, we compare several supported simulators under a shared interaction protocol and showcase its extensibility by prototyping a new simulator variant with minimal additional code. By consolidating existing work into a single reproducible pipeline, our framework eliminates much of the infrastructure overhead that currently fragments this research and accelerates the development of new methods. Our code and data are publicly available via https://github.com/Sahandfer/PatientHub.
Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；TUM University Hospital（慕尼黑技术大学医院）；Google DeepMind（谷歌DeepMind）；Google Research（谷歌研究）；Imperial College London（伦敦帝国学院）
Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering
School of Software, Xi’an Jiaotong University（西安交通大学软件学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；College of Computer Science, Sichuan University（四川大学计算机学院）；Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, China（教育部机器学习与工业智能工程研究中心，中国）
Multi-Stage Patient Role-Playing Framework for Realistic Clinical Interactions
College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University（计算机科学与技术学院、符号计算与知识工程重点实验室、教育部、吉林大学）；College of Software, Jilin University（软件学院、吉林大学）；Gastrointestinal Endoscopy Center, The First Hospital of Jilin University（吉林大学第一医院消化内窥镜中心）
DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge
Center for Healthcare Organization and Implementation Research, VA Bedford Health Care（VA贝德福德医疗保健中心医疗组织与实施研究中心）；Manning College of Information and Computer Sciences, UMass Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）；Miner School of Computer and Information Sciences, UMass Lowell（马萨诸塞大学洛厄尔分校迈纳计算机与信息科学学院）；Department of Internal Medicine, Chung-Ang University（韩国中央大学内科学系）
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）
SSR: Can Simulated Patients Learn to Stigmatize Themselves? Modeling Self-Stigma through Internal Monologue
Shanghai Jiao Tong University（上海交通大学）；X-LANCE Lab, Dept. of Computer Science and Engineering（X-LANCE实验室，计算机科学与工程系）；MoE Key Lab of Artificial Intelligence, AI Institute（教育部人工智能重点实验室，人工智能研究院）
Patient-Zero: Scaling Synthetic Patient Agents to Real-World Distributions without Real Patient Data
Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University（计算机科学与技术系，人工智能学院，清华大学）；Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院，清华大学）
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）；Department of Psychology, Princeton University（普林斯顿大学心理学系）；School of Social Work, University of Pittsburgh（匹兹堡大学社会工作学院）；Department of Psychiatry and Behavioral Sciences, Stanford University（斯坦福大学精神病学与行为科学系）
