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Source note3 min read

Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation

Source

arXiv

arxiv.org/abs/2607.15202v1

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Plain-Language Brief

Researchers have proposed a self-evolving framework to improve the quality of depression symptom annotations in AI systems for mental health research. The framework combines large language models with human expertise, operating in three stages: evidence selection, DSM-5-TR analysis, and case synthesis. It aims to produce explainable, DSM-5-TR-aligned datasets, not for clinical diagnosis. The framework's design includes a dual-memory architecture that internalizes expert feedback for future improvements. A pilot study suggests the approach improves annotation consistency, explainability, and reduces manual revision effort.

What the Document Says

  • The research focuses on improving the quality of depression symptom annotations in AI systems for mental health research.
  • The authors propose a self-evolving annotation framework for Major Depressive Disorder (MDD) that combines large language models with expert verification.
  • The framework operates in three stages: candidate evidence selection, criterion-level DSM-5-TR analysis, and case-level synthesis.
  • The goal is to support the construction of explainable, DSM-5-TR-aligned datasets, not for clinical diagnosis.
  • A dual-memory architecture is designed to internalize expert feedback and iteratively improve future annotations without retraining.
  • In a pilot study, the proposed approach improves annotation consistency and explainability while reducing manual revision effort.

Key Concepts Mentioned

  • depression symptom annotation
  • artificial intelligence (XAI)
  • Major Depressive Disorder (MDD)
  • large language model
  • DSM-5-TR
  • expert verification
  • annotation consistency
  • explainability
  • manual revision effort

Claims Made by the Document

  • Improving the quality of depression symptom annotations in AI systems for mental health research is a major bottleneck.
  • The proposed self-evolving framework improves annotation consistency and explainability while reducing manual revision effort.

Evidence Used

  • The text mentions the challenge of poor annotation quality and the improvement achieved by the proposed approach.

Methods / Approach

  • Combining large language models with expert verification
  • Three-stage operation: evidence selection, DSM-5-TR analysis, and case synthesis
  • Dual-memory architecture for internalizing expert feedback

Limitations Stated by the Authors

  • The evaluation across multiple feedback cycles is left to future work.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The research is limited by the quality and quantity of data available for training the large language models.
  • The pilot study may not be representative of real-world scenarios.

Open Questions

  • What is the overall effectiveness of the proposed framework in producing high-quality, explainable datasets?
  • How does the framework perform across various mental health disorders beyond Major Depressive Disorder?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The research does not provide specific details about the large language models used.
  • The study does not include a comparison with other annotation methods.

Raw Abstract / Excerpt

Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), limiting both transparency and downstream model interpretability. We propose a self-evolving, expert-in-the-loop annotation framework for Major Depressive Disorder (MDD) that combines large language model (LLM)-assisted labeling with expert verification. The framework is intended to support the construction of explainable, DSM-5-TR-aligned datasets rather than to perform clinical diagnosis. It operates in three stages: candidate evidence selection from textual records, criterion-level DSM-5-TR analysis, and case-level synthesis that produces label-level diagnostic and severity annotations. A dual-memory architecture, composed of Example Memory and Reflection Memory, is designed to internalize expert feedback and iteratively improve future annotations without retraining. We describe this mechanism and leave its evaluation across multiple feedback cycles to future work. In addition to final labels, the framework exports clinical evidence, reasoning traces, and edit histories, enabling comprehensive auditability. In a pilot study using expert-reviewed samples, the proposed approach improves annotation consistency and explainability while reducing manual revision effort.