MAGI
MAGI is a multi-agent system designed to automate structured psychiatric interviews by operationalizing the MINI (Mini International Neuropsychiatric Interview) protocol. It involves 4 specialized agents: navigation, question generation, judgment, and diagnosis. Other highlights:
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Multi-Agent Clinical Workflow: MAGI is built with a navigation agent (interview flow control), a question agent (dynamic, empathetic probing), a judgment agent (response validation), and a diagnosis agent using Psychometric CoT to trace diagnoses explicitly to MINI/DSM-5 criteria.
Explainable Reasoning (PsyCoT): Instead of treating diagnoses as opaque outputs, PsyCoT decomposes psychiatric reasoning into symptom anchoring, syndromal validation, and evidence binding. This helps with auditability for each diagnostic conclusion. CoT put to great use.
Results: Evaluated on 1,002 real-world interviews, MAGI outperforms baselines (Direct prompting, Role-play, Knowledge-enhanced, and MINI-simulated LLMs) across relevance, accuracy, completeness, and guidance.
Strong Clinical Agreement: Diagnostic evaluations show PsyCoT consistently improves F1 scores, accuracy, and Cohen’s κ across disorders like depression, generalized anxiety, social anxiety, and suicide risk, reaching clinical-grade reliability (κ 0.8) in high-risk tasks.