MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis
It addresses the problem of subjective and data-limited psychiatric diagnosis for mental health professionals, offering an incremental advance with a specialized tool and dataset.
The paper tackles challenges in AI-assisted psychiatric diagnosis by introducing MoodAngels, a multi-agent framework that improves accuracy, achieving 12.3% higher accuracy than GPT-4o on real-world cases, and MoodSyn, a synthetic dataset of 1,173 cases for privacy-preserving research.
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.