MAGI : Multi-Agent Guided Interview for Psychiatric Assessment (2025)

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.

## introduction mental health disorders pose a significant global challenge, and accurate and timely diagnosis is crucial for effective intervention (american psychiatric association, 2013). clinical interviews, particularly structured instruments, are gold standards for psychiatric assessment @xcite . the mini international neuropsychiatric interview (mini) @xcite is a widely adopted structured interview protocol, providing a systematic approach to assess major psychiatric disorders. however, their manual administration is time-consuming and resource-intensive, creating a * corresponding author. bottleneck in mental healthcare access and limiting the widespread use in real-world settings. the advent of large language models (llms) @xcite @xcite offers promising avenues to automate and enhance mental health support processes, potentially democratizing access to care @xcite . while llm-based mental health tools demonstrate conversational fluency @xcite , three fundamental mismatches with clinical practice impede their adoption. (1) diagnostic framework deviation: current open-ended llm dialogues frequently diverge from established clinical standards like dsm-5, particularly struggling with nuanced differential diagnoses @xcite . (2) rigid interaction patterns: existing systems employ inflexible questioning templates that prove inadequate when participants exhibit emotional distress or require clarifications, contradicting evidence-based interviewing principles @xcite . (3) limited diagnosis transparency: most systems operate as "black boxes", providing little insight into their diagnostic reasoning process. this opacity significantly undermines trust and hinders clinical adoption, especially in high-stakes healthcare settings @xcite . to bridge these gaps, we present magi (multi-agent guided interview), a clinical logic-driven framework that operationalizes the mini interview through four coordinated agents. consider a depressive episode screening shown in figure 1 : when a participant reports feeling hopeless, the navigation agent activates the diagnostic nodes systematically from duration (a2b) to appetite (a3a) and sleep (a3b), dynamically managing interview progression. guided by this structure, the question agent adaptively switches between three strategies: structure-driven probing ("how long has this lasted?"), explaining for ambiguous concepts (clarifying significant weight change), and empathetic support when detecting distress @xcite . the judgment agent continuously validates responses against mini criteria, iterating until all necessary evidence is collected. finally, the diagnosis agent synthesizes the accumulated evidence into formal diagnostic conclusions through psychometric chain-of-thought (psycot), a clinical reasoning paradigm that explicitly connects symptom observations to diagnostic criteria via intermediate psychiatric constructs. through a school-based study, we collect 1,002 clinical interviews covering depression, generalized anxiety, social anxiety, and suicide risk. with dual-expert annotation from licensed psychologists, our experiments demonstrate magi's clinical validity, achieving high agreement with expert diagnoses while providing transparent diagnostic traces absent in prior llm approaches @xcite . our contributions are threefold: ## methodology magi transforms the mini interview into a collaborative multi-agent process, where four specialized components interact through clinically constrained data flows. the navigation agent ( §3.1) governs interview progression by enforcing mini's branching logic, while the question agent ( §3.2) dynamically generates utterances that balance diagnostic probing with participant support. participant responses are analyzed by the judgment agent ( §3.3) to determine symptom validity, with accumulated evidence processed by the diagnosis agent ( §3.4) to generate dsm-5 compliant conclusions through structured reasoning traces. this architecture ensures adherence to psychiatric assessment protocols while maintaining conversational adaptability. ## conclusion this work addresses the critical challenge of aligning llm-powered mental health assessment with clinical diagnostic standards through magi, the first multi-agent framework that operationalizes structured psychiatric interviews. by translating the mini protocol into dynamic computational workflows, our framework overcomes three fundamental limitations of existing approaches: (1) systematic adherence to diagnostic logic through navigation and judgment agents ensures clinical validity, (2) adaptive questioning strategies enable human-like conversational flexibility while maintaining assessment integrity, and (3) the novel psy-cot mechanism provides transparent diagnostic reasoning that maps symptoms to dsm-5 criteria through intermediate psychiatric constructs. extensive evaluation across 1,002 real-world cases demonstrates magi's superior diagnostic agreement with expert clinicians while maintaining natural interaction flow.