SimClinician: A Multimodal Simulation Testbed for Reliable Psychologist AI Collaboration in Mental Health Diagnosis
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914190852096000 |
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| author | Cenacchi, Filippo Cao, Longbing Richards, Deborah |
| author_facet | Cenacchi, Filippo Cao, Longbing Richards, Deborah |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_08953 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SimClinician: A Multimodal Simulation Testbed for Reliable Psychologist AI Collaboration in Mental Health Diagnosis Cenacchi, Filippo Cao, Longbing Richards, Deborah Human-Computer Interaction Artificial Intelligence 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. |
| title | SimClinician: A Multimodal Simulation Testbed for Reliable Psychologist AI Collaboration in Mental Health Diagnosis |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2512.08953 |