标题： 一种用于精神病诊断的检索增强型多智能体框架 显示英文标题

标题： A Retrieval-Augmented Multi-Agent Framework for Psychiatry Diagnosis

摘要： 人工智能在精神疾病诊断中的应用面临重大挑战，包括心理健康评估的主观性、不同疾病之间的症状重叠以及隐私限制导致的数据可用性问题。 为了解决这些问题，我们提出了 MoodAngels，这是首个专门用于情绪障碍诊断的多智能体框架。 我们的方法结合了临床评估的细粒度分析与结构化验证过程，从而能够更准确地解读复杂的精神病学数据。 作为该框架的补充，我们推出了 MoodSyn，这是一个包含 1,173 个合成精神病案例的开源数据集，在确保患者隐私的同时保留了临床有效性。 实验结果显示，MoodAngels 在性能上优于传统方法，其中我们的基线智能体在真实案例上的准确性比 GPT-4o 高出 12.3%，而完整的多智能体系统则进一步提升了表现。 在 MoodSyn 数据集中进行的评估展示了卓越的保真度，不仅精确再现了原始数据中的核心统计模式和复杂关系，还维持了对机器学习应用的强大实用性。 这些贡献共同提供了一种先进的诊断工具和计算精神病学的重要研究资源，填补了 AI 辅助心理健康评估中的关键空白。 显示英文摘要

摘要： The application of AI in psychiatric diagnosis faces significant challenges, including the subjective nature of mental health assessments, symptom overlap across disorders, and privacy constraints limiting data availability. To address these issues, we present MoodAngels, the first specialized multi-agent framework for mood disorder diagnosis. Our approach combines granular-scale analysis of clinical assessments with a structured verification process, enabling more accurate interpretation of complex psychiatric data. Complementing this framework, we introduce MoodSyn, an open-source dataset of 1,173 synthetic psychiatric cases that preserves clinical validity while ensuring patient privacy. Experimental results demonstrate that MoodAngels outperforms conventional methods, with our baseline agent achieving 12.3% higher accuracy than GPT-4o on real-world cases, and our full multi-agent system delivering further improvements. Evaluation in the MoodSyn dataset demonstrates exceptional fidelity, accurately reproducing both the core statistical patterns and complex relationships present in the original data while maintaining strong utility for machine learning applications. Together, these contributions provide both an advanced diagnostic tool and a critical research resource for computational psychiatry, bridging important gaps in AI-assisted mental health assessment.