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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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Source

Plain-Language Brief

Researchers have proposed a new framework to improve the quality of annotations in depression-related datasets for artificial intelligence systems in mental health research. The framework uses a large language model (LLM) to assist with labeling and expert verification, aiming to create explainable, DSM-5-TR aligned datasets without clinical diagnosis. The system operates in three stages: evidence selection, criterion-level analysis, and case-level synthesis. It also includes a dual-memory architecture that learns from expert feedback to improve future annotations over time. In a pilot study, the proposed approach showed improvements in annotation consistency, explainability, and reduction of manual revision effort.

What the Document Says

  • The quality of annotations is a significant issue in building reliable AI systems for mental health research.
  • In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with DSM-5-TR criteria.
  • A new framework is proposed that combines LLM-assisted labeling and expert verification for Major Depressive Disorder (MDD).
  • The goal is to support the construction of explainable, DSM-5-TR aligned datasets rather than performing clinical diagnosis.
  • The framework operates in three stages: candidate evidence selection, criterion-level DSM-5-TR analysis, and case-level synthesis that produces label-level diagnostic and severity annotations.
  • A dual-memory architecture is designed to internalize expert feedback and iteratively improve future annotations without retraining.
  • The system exports clinical evidence, reasoning traces, and edit histories for comprehensive auditability.
  • In a pilot study, the proposed approach improved annotation consistency and explainability while reducing manual revision effort.

Key Concepts Mentioned

  • Annotation quality
  • Artificial Intelligence (XAI) systems
  • Mental health research
  • Depression-related datasets
  • Structured evidence
  • Symptom-level justification
  • DSM-5-TR criteria alignment
  • Major Depressive Disorder (MDD)
  • Large language model (LLM)-assisted labeling
  • Expert verification
  • Explainable datasets
  • Clinical diagnosis
  • Candidate evidence selection
  • Criterion-level DSM-5-TR analysis
  • Case-level synthesis
  • Label-level diagnostic and severity annotations
  • Dual-memory architecture
  • Example Memory
  • Reflection Memory
  • Expert feedback
  • Iterative improvement
  • Clinical evidence
  • Reasoning traces
  • Edit histories
  • Comprehensive auditability
  • Pilot study

Claims Made by the Document

  • Improving annotation quality is crucial for building reliable AI systems in mental health research.
  • The proposed framework can support the construction of explainable, DSM-5-TR aligned datasets.
  • The framework improves annotation consistency and explainability while reducing manual revision effort.

Evidence Used

  • The text states that annotation quality is a major bottleneck in building reliable AI systems for mental health research.
  • The text describes the proposed self-evolving, expert-in-the-loop annotation framework and its intended purpose.
  • The text mentions improvements observed in a pilot study using expert-reviewed samples.

Methods / Approach

  • LLM-assisted labeling
  • Expert verification
  • Candidate evidence selection
  • Criterion-level DSM-5-TR analysis
  • Case-level synthesis
  • Dual-memory architecture with Example Memory and Reflection Memory

Limitations Stated by the Authors

The authors mention that they describe the mechanism but leave its evaluation across multiple feedback cycles to future work.

Limitations Noted by the Agent

Agent interpretation — not from the source.

Unknown, as it's not explicitly stated in the source.

Open Questions

  • What specific large language model was used in this study?
  • How does the system handle disagreements between the LLM and experts?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The authors do not specify which large language model they use. The system likely handles disagreements between the LLM and experts through expert verification, but the details are not provided in this source.

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.