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2/ The system includes four agents: (1) a qualitative assessment agent that identifies risk factors, (2) a judge agent that evaluates qualitative assessment, (3) a quantitative assessment agent that predicts clinical scores, (4) a meta-review agent that integrates information and estimates severity.

3/ The qualitative assessment agent produced specific, coherent, complete, and clinically meaningful assessments, confirmed by both a human reviewer and the judge agent. The feedback loop effectively improved all evaluation metrics.

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5/ The meta-review agent achieved 78% accuracy in binary classification, remarkably matching the performance of a human evaluator. This system may serve as an interpretable, scalable decision-support tool, offering insights into patients' mental health conditions.

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Can AI agents assess mental disorders like human experts? Our #ML4H2025 paper presents an LLM-based multi-agent system for assessing depression from clinical interviews. It combines four collaborative agents, iterative self-refinement, and few-shot prompting, achieving human-level performance.

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Congratulations to Masoud Seraji for receiving the Georgia State University Dean’s Graduate Research Grant & the Trainee travel award from International Society for Magnetic Resonance in Medicine (ISMRM)!