注释质量是为心理健康研究构建可靠且可解释的人工智能 (XAI) 系统的主要瓶颈。在抑郁症相关数据集中，, 标签通常在没有结构化证据, 症状水平合理性, 或与精神疾病诊断和统计手册, 第五版, 文本修订(DSM-5-TR), 标准的可追踪一致性的情况下分配，这限制了透明度和下游模型的可解释性。我们针对重度抑郁症 (MDD) 提出了一个自我进化的 , 专家循环注释框架，该框架将大型语言模型 (LLM) 辅助标记与专家验证相结合。该框架旨在支持构建可解释的, DSM-5-TR 对齐数据集，而不是执行临床诊断。它分三个阶段运行: 从文本记录中选择候选证据, 标准级 DSM-5-TR 分析, 和生成标签级诊断和严重性注释的病例级综合。由示例内存和反射内存, 组成的双内存架构, 旨在内化专家反馈并迭代改进未来的注释，而无需重新训练。我们描述了这种机制，并将其跨多个反馈周期的评估留给未来的工作。除了最终标签, 之外，该框架还导出临床证据, 推理痕迹, 并编辑历史,，从而实现全面的可审计性。在使用专家评审样本, 的试点研究中，所提出的方法提高了注释的一致性和可解释性，同时减少了手动修改工作。

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.

科目: 人工智能 (cs.AI); 人机交互 (cs.HC); 多代理系统 (cs.MA); 多媒体 (cs.MM)

Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA); Multimedia (cs.MM)