Researchers have developed a novel framework to improve the annotation of depression symptoms for AI systems, addressing the common issue of labels lacking structured evidence or clear alignment with diagnostic criteria. This self-evolving, expert-in-the-loop system combines large language model assistance with human verification to create more reliable and explainable datasets for mental health research. The framework operates in three stages, including evidence selection, DSM-5-TR analysis, and case-level synthesis, and features a dual-memory architecture to internalize expert feedback for iterative improvement without retraining. AI
IMPACT This framework could lead to more reliable and interpretable AI models for mental health research by improving the quality of training data.
RANK_REASON The cluster contains an academic paper detailing a new framework for AI annotation.
Read on arXiv cs.MA (Multiagent) →
- DSM-5-TR
- Example Memory
- large language model
- major depressive disorder
- Reflection Memory
- Truong Thanh Hung Nguyen
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