SimClinician模拟心理医生与AI协作诊断，提升决策可信度。
SimClinician: A Multimodal Simulation Testbed for Reliable Psychologist AI Collaboration in Mental Health Diagnosis
- 整合音频、文本、眼神表情的多模态仪表板
- 确认环节使采纳率提升23%，误报率低于9%
- 适合研究人机协作心理诊断界面设计
基于AI的心理健康诊断常以基准准确率评判，但实际价值取决于心理医生是否采纳、调整或拒绝AI建议。心理健康诊断尤为复杂：决策连续，受患者语气、停顿、用词和非语言行为等线索影响。现有研究极少探讨诊断界面设计如何影响医生判断，缺乏可靠测试基础。我们提出SimClinician，一个交互式仿真平台，将患者数据转化为心理医生与AI协同诊断的场景。包含三大贡献：(1) 集成音频、文本和注视-表情模式的仪表板；(2) 虚拟化身模块，呈现去标识化的行为动态供分析；(3) 决策层将AI输出映射为多模态证据，支持医生审阅推理并输入诊断。在E-DAIC语料库（276段临床访谈，扩展至48万次仿真）上测试显示，增加确认步骤可使采纳率提升23%，误报率维持在9%以下，且保持流畅交互流程。
原文摘要 · Abstract (English)
AI based mental health diagnosis is often judged by benchmark accuracy, yet in practice its value depends on how psychologists respond whether they accept, adjust, or reject AI suggestions. Mental health makes this especially challenging: decisions are continuous and shaped by cues in tone, pauses, word choice, and nonverbal behaviors of patients. Current research rarely examines how AI diagnosis interface design influences these choices, leaving little basis for reliable testing before live studies. We present SimClinician, an interactive simulation platform, to transform patient data into psychologist AI collaborative diagnosis. Contributions include: (1) a dashboard integrating audio, text, and gaze-expression patterns; (2) an avatar module rendering de-identified dynamics for analysis; (3) a decision layer that maps AI outputs to multimodal evidence, letting psychologists review AI reasoning, and enter a diagnosis. Tested on the E-DAIC corpus (276 clinical interviews, expanded to 480,000 simulations), SimClinician shows that a confirmation step raises acceptance by 23%, keeping escalations below 9%, and maintaining smooth interaction flow.
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