MAGI: Multi-Agent Guided Interview for Psychiatric Assessment
Guanqun Bi, Zhuang Chen, Zhoufu Liu, Hongkai Wang, Xiyao Xiao, Yuqiang Xie, Wen Zhang, Yongkang Huang
Abstract
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.
BibTeX
@inproceedings{bi-etal-2025-magi,
title = "{MAGI}: Multi-Agent Guided Interview for Psychiatric Assessment",
author = "Bi, Guanqun and
Chen, Zhuang and
Liu, Zhoufu and
Wang, Hongkai and
Xiao, Xiyao and
Xie, Yuqiang and
Zhang, Wen and
Huang, Yongkang and
Chen, Yuxuan and
Peng, Libiao and
Huang, Minlie",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1278/",
doi = "10.18653/v1/2025.findings-acl.1278",
pages = "24898--24921",
ISBN = "979-8-89176-256-5"
}