# PATIENT-Psi: Using Large Language Models to Simulate Patients for Training Mental Health Professionals

arXiv: https://arxiv.org/abs/2405.19660 · HTML: https://mlsanity.com/paper/2405.19660/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 59 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents](https://mlsanity.com/paper/2403.08715/) (2024)
- [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://mlsanity.com/paper/2305.02466/) (2023)
- [A Computational Framework for Behavioral Assessment of LLM Therapists](https://mlsanity.com/paper/2401.00820/) (2024)
- [Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring](https://mlsanity.com/paper/2310.15461/) (2023)
- [CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principles](https://mlsanity.com/paper/2305.13272/) (2023)
- [Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting](https://mlsanity.com/paper/2310.07146/) (2023)
- [Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals](https://mlsanity.com/paper/2308.15192/) (2023)
- [Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback](https://mlsanity.com/paper/2305.08982/) (2023)
- [Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platforms](https://mlsanity.com/paper/2004.04999/) (2020)
- [Social Skill Training with Large Language Models](https://mlsanity.com/paper/2404.04204/) (2024)
- [IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction](https://mlsanity.com/paper/2402.12556/) (2024)
- [EmoMent: An Emotion Annotated Mental Health Corpus from two South Asian Countries](https://mlsanity.com/paper/2208.08486/) (2022)

## Built on by (most cited first, 21 of 21)

- [LLM Generated Persona is a Promise with a Catch](https://mlsanity.com/paper/2503.16527/) (2025)
- [TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Students](https://mlsanity.com/paper/2410.04078/) (2024)
- [EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety](https://mlsanity.com/paper/2504.09689/) (2025)
- [Technological folie `a deux: Feedback Loops Between AI Chatbots and Mental Illness](https://mlsanity.com/paper/2507.19218/) (2025)
- [MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems](https://mlsanity.com/paper/2505.18943/) (2025)
- [MedDialogRubrics: A Comprehensive Benchmark and Evaluation Framework for Multi-turn Medical Consultations in Large Language Models](https://mlsanity.com/paper/2601.03023/) (2026)
- [MindEval: Benchmarking Language Models on Multi-turn Mental Health Support](https://mlsanity.com/paper/2511.18491/) (2025)
- [How Strategic Agents Respond: Comparing Analytical Models with LLM-Generated Responses in Strategic Classification](https://mlsanity.com/paper/2501.16355/) (2025)
- [MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders](https://mlsanity.com/paper/2410.06845/) (2024)
- [Quantifying Risk Propensities of Large Language Models: Ethical Focus and Bias Detection through Role-Play](https://mlsanity.com/paper/2411.08884/) (2024)
- [Free Lunch for User Experience: Crowdsourcing Agents for Scalable User Studies](https://mlsanity.com/paper/2505.22981/) (2025)
- [DS@GT at eRisk 2025: From prompts to predictions, benchmarking early depression detection with conversational agent based assessments and temporal attention models](https://mlsanity.com/paper/2507.10958/) (2025)
- [From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity](https://mlsanity.com/paper/2510.25232/) (2025)
- [TherapyGym: Evaluating and Aligning Clinical Fidelity and Safety in Therapy Chatbots](https://mlsanity.com/paper/2603.18008/) (2026)
- [An LLM-based Simulation Framework for Embodied Conversational Agents in Psychological Counseling](https://mlsanity.com/paper/2410.22041/) (2024)
- [When and How to Integrate Multimodal Large Language Models in College Psychotherapy: Perspectives from Multi-stakeholders](https://mlsanity.com/paper/2502.00229/) (2025)
- [First, Do No Harm: AI Supervisor Scaffolds Novice Growth in Counselor Education](https://mlsanity.com/paper/2508.09042/) (2025)
- [A Voice-Enabled Virtual Patient System for Interactive Training in Standardized Clinical Assessment](https://mlsanity.com/paper/2511.00709/) (2025)
- [Can we use LLMs to bootstrap reinforcement learning? -- A case study in digital health behavior change](https://mlsanity.com/paper/2511.17630/) (2025)
- [The TEA Nets framework combines AI and cognitive network science to model targets, events and actors in text](https://mlsanity.com/paper/2604.27673/) (2026)
- [Multi-Objective Aligned Small Language Model Framework for SUD Patient Dialogue Generation](https://mlsanity.com/paper/2610.09209/) (2026)

## Abstract (arXiv)

Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and actual real-world patient practice. To help bridge this gap, we propose PATIENT-Psi, a novel patient simulation framework for cognitive behavior therapy (CBT) training. To build PATIENT-Psi, we construct diverse patient cognitive models based on CBT principles and use large language models (LLMs) programmed with these cognitive models to act as a simulated therapy patient. We propose an interactive training scheme, PATIENT-Psi-TRAINER, for mental health trainees to practice a key skill in CBT -- formulating the cognitive model of the patient -- through role-playing a therapy session with PATIENT-Psi. To evaluate PATIENT-Psi, we conducted a comprehensive user study of 13 mental health trainees and 20 experts. The results demonstrate that practice using PATIENT-Psi-TRAINER enhances the perceived skill acquisition and confidence of the trainees beyond existing forms of training such as textbooks, videos, and role-play with non-patients. Based on the experts' perceptions, PATIENT-Psi is perceived to be closer to real patient interactions than GPT-4, and PATIENT-Psi-TRAINER holds strong promise to improve trainee competencies. Our code and data are released at urlhttps://github.com/ruiyiw/patient-psi.