MAGI: Multi-Agent Guided Interview for Psychiatric Assessment
🤖 AI Summary
Current large language model (LLM)-based approaches struggle to align with standardized psychiatric diagnostic protocols, limiting the clinical utility of automated mental health interviews for improving access to care. To address this, we propose the first multi-agent system for automating the Mini-International Neuropsychiatric Interview (MINI), uniquely decomposing its clinical protocol into four synergistic agent roles: clinical logic navigation, adaptive question generation, response classification, and diagnostic reasoning. We introduce Psychometric Chain-of-Thought (PsyCoT), a novel mechanism enabling interpretable mapping from symptom representations to DSM/ICD diagnostic criteria. The system integrates decision-tree–guided navigation, empathetic dialogue generation, and structured response validation. Evaluated on 1,002 real-world participants, it performs assessments for major depressive disorder, generalized anxiety disorder, social anxiety disorder, and suicide risk. Experiments demonstrate significant improvements in clinical consistency (+23.6%), conversational adaptability (+31.4%), and diagnostic interpretability.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Automating psychiatric interviews with clinical protocol alignment
Transforming MINI into multi-agent computational workflows
Enhancing mental health assessment via explainable AI reasoning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-agent system for psychiatric interview automation
Dynamic clinical logic navigation via four agents
Explainable PsyCoT traces mapping symptoms to criteria
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