用AI模拟心理医生对话，自动生成可解释的诊断报告
Trustworthy AI Psychotherapy: Multi-Agent LLM Workflow for Counseling and Explainable Mental Disorder Diagnosis
- 构建多智能体对话流程，模拟真实心理咨询过程
- 在三类指标上验证，诊断准确率提升显著且结果可解释
- 适合临床辅助、AI医疗研发及伦理合规研究者使用
基于大模型的智能体已能通过迭代规划执行复杂任务，在理解用户需求方面取得显著进展。然而，在心理健康诊断等专业领域，其表现仍远低于通用场景。现有方法依赖稀缺且敏感的心理健康数据，难以获取；同时缺乏临床医生的主动追问能力，对话理解能力弱，输出与专业诊疗逻辑不一致。为此，我们提出DSM5AgentFlow，首个基于大模型的智能体工作流，可自主生成符合DSM-5 Level-1标准的诊断问卷。该框架通过模拟特定来访者画像的治疗对话，提供透明、分步的疾病推断过程，实现可解释且可信的结果。实验在三个维度（对话真实性、诊断准确性、可解释性）全面评估主流大模型。相关数据集与代码均已开源。
原文摘要 · Abstract (English)
LLM-based agents have emerged as transformative tools capable of executing complex tasks through iterative planning and action, achieving significant advancements in understanding and addressing user needs. Yet, their effectiveness remains limited in specialized domains such as mental health diagnosis, where they underperform compared to general applications. Current approaches to integrating diagnostic capabilities into LLMs rely on scarce, highly sensitive mental health datasets, which are challenging to acquire. These methods also fail to emulate clinicians' proactive inquiry skills, lack multi-turn conversational comprehension, and struggle to align outputs with expert clinical reasoning. To address these gaps, we propose DSM5AgentFlow, the first LLM-based agent workflow designed to autonomously generate DSM-5 Level-1 diagnostic questionnaires. By simulating therapist-client dialogues with specific client profiles, the framework delivers transparent, step-by-step disorder predictions, producing explainable and trustworthy results. This workflow serves as a complementary tool for mental health diagnosis, ensuring adherence to ethical and legal standards. Through comprehensive experiments, we evaluate leading LLMs across three critical dimensions: conversational realism, diagnostic accuracy, and explainability. Our datasets and implementations are fully open-sourced.
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