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

TL;DR

SimClinician is a simulation platform that integrates multimodal patient data and AI reasoning to study and improve psychologist-AI collaboration in mental health diagnosis, emphasizing interface design and decision-making processes.

Contribution

It introduces a comprehensive simulation environment with multimodal data integration, avatar rendering, and decision mapping to facilitate reliable testing of AI-psychologist interactions.

Findings

Confirmation step increases AI acceptance by 23%.

Escalation rates remain below 9%.

Interaction flow remains smooth during simulations.

Abstract

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.