WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis
Yuqi Wu, Guangya Wan, Jingjing Li, Shengming Zhao, Lingfeng Ma, Tianyi Ye, Ion Pop, Yanbo Zhang, Jie Chen

TL;DR
WiseMind is a multi-agent AI framework inspired by Dialectical Behavior Therapy that improves psychiatric diagnosis accuracy and empathy by integrating evidence-based reasoning and emotional communication, reducing hallucinations and enhancing trust.
Contribution
This work introduces WiseMind, a novel multi-agent system combining clinical reasoning and empathetic communication guided by DSM-5 knowledge graphs for psychiatric assessment.
Findings
Achieves 85.6% top-1 diagnostic accuracy in real and simulated interactions.
Outperforms state-of-the-art LLM methods by 15-54 percentage points.
Generates clinically sound and psychologically supportive responses validated by psychiatrists.
Abstract
Large Language Models (LLMs) offer promising opportunities to support mental healthcare workflows, yet they often lack the structured clinical reasoning needed for reliable diagnosis and may struggle to provide the emotionally attuned communication essential for patient trust. Here, we introduce WiseMind, a novel multi-agent framework inspired by the theory of Dialectical Behavior Therapy designed to facilitate psychiatric assessment. By integrating a "Reasonable Mind" Agent for evidence-based logic and an "Emotional Mind" Agent for empathetic communication, WiseMind effectively bridges the gap between instrumental accuracy and humanistic care. Our framework utilizes a Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)-guided Structured Knowledge Graph to steer diagnostic inquiries, significantly reducing hallucinations compared to standard prompting methods.…
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