这篇论文提出一种结合LLM和专家验证的抑郁症标注框架，能自动生成符合DSM-5-TR标准的推理轨迹，减少人工修订。适合研究可解释AI和心理健康数据标注的人。
该论文提出一个自进化、专家参与的MDD症状标注框架，结合LLM辅助标注与专家验证，旨在构建与DSM-5-TR对齐的可解释数据集。框架分为三个阶段：候选证据选择、DSM-5-TR准则分析、病例级综合诊断与严重性标注。采用双记忆架构（示例记忆和反思记忆）内化专家反馈，无需重训即可迭代改进。初步研究表明，该方法在专家评审样本上提升了标注一致性和可解释性，并减少了人工修订量。
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
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.