# SimClinician: A Multimodal Simulation Testbed for Reliable Psychologist AI Collaboration in Mental Health Diagnosis

arXiv: https://arxiv.org/abs/2512.08953 · HTML: https://mlsanity.com/paper/2512.08953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 28 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations](https://mlsanity.com/paper/2302.02187/) (2023)
- [A Unifying Theory of Distance from Calibration](https://mlsanity.com/paper/2211.16886/) (2022)
- [Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care](https://mlsanity.com/paper/2302.00096/) (2023)
- [Metrics of calibration for probabilistic predictions](https://mlsanity.com/paper/2205.09680/) (2022)
- [Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation](https://mlsanity.com/paper/2003.00813/) (2020)
- [Comparing Zealous and Restrained AI Recommendations in a Real-World Human-AI Collaboration Task](https://mlsanity.com/paper/2410.11860/) (2024)
- [Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges](https://mlsanity.com/paper/2407.16804/) (2024)
- [Calibrate: Interactive Analysis of Probabilistic Model Output](https://mlsanity.com/paper/2207.13770/) (2022)
- [On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook](https://mlsanity.com/paper/2410.08793/) (2024)
- [PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services](https://mlsanity.com/paper/2408.00950/) (2024)

## Abstract (arXiv)

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.