Automating structured clinical interviews could revolutionize mental healthcare accessibility, yet existing large language models (LLMs) approaches fail to align with psychiatric diagnostic protocols. We present MAGI, the first framework that transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational workflows through coordinated multi-agent collaboration. MAGI dynamically navigates clinical logic via four specialized agents: 1) an interview tree guided navigation agent adhering to the MINI's branching structure, 2) an adaptive question agent blending diagnostic probing, explaining, and empathy, 3) a judgment agent validating whether the response from participants meet the node, and 4) a diagnosis Agent generating Psychometric Chain-of- Thought (PsyCoT) traces that explicitly map symptoms to clinical criteria. Experimental results on 1,002 real-world participants covering depression, generalized anxiety, social anxiety and suicide shows that MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning.
@article{arxiv.2504.18260,
title = {MAGI: Multi-Agent Guided Interview for Psychiatric Assessment},
author = {Guanqun Bi and Zhuang Chen and Zhoufu Liu and Hongkai Wang and Xiyao Xiao and Yuqiang Xie and Wen Zhang and Yongkang Huang and Yuxuan Chen and Libiao Peng and Yi Feng and Minlie Huang},
journal= {arXiv preprint arXiv:2504.18260},
year = {2025}
}