Depression poses significant challenges to patients and healthcare organizations, necessitating efficient assessment methods. Existing paradigms typically focus on a patient-doctor way that overlooks multi-role interactions, such as family involvement in the evaluation and caregiving process. Moreover, current automatic depression detection (ADD) methods usually model depression detection as a classification or regression task, lacking interpretability for the decision-making process. To address these issues, we developed InterMind, a doctor-patient-family interactive depression assessment system empowered by large language models (LLMs). Our system enables patients and families to contribute descriptions, generates assistive diagnostic reports for doctors, and provides actionable insights, improving diagnostic precision and efficiency. To enhance LLMs' performance in psychological counseling and diagnostic interpretability, we integrate retrieval-augmented generation (RAG) and chain-of-thoughts (CoT) techniques for data augmentation, which mitigates the hallucination issue of LLMs in specific scenarios after instruction fine-tuning. Quantitative experiments and professional assessments by clinicians validate the effectiveness of our system.

翻译：抑郁症对患者和医疗机构构成重大挑战，亟需高效的评估方法。现有范式通常聚焦于医患二元模式，忽视了评估与照护过程中的多角色交互，例如家属的参与。此外，当前自动抑郁症检测方法通常将抑郁识别建模为分类或回归任务，其决策过程缺乏可解释性。为解决这些问题，我们开发了InterMind——一个基于大语言模型的医-患-家属交互式抑郁症评估系统。本系统支持患者与家属共同提供描述，为医生生成辅助诊断报告并提供可操作的见解，从而提升诊断精确性与效率。为增强大语言模型在心理咨询与诊断可解释性方面的表现，我们融合检索增强生成与思维链技术进行数据增强，这缓解了指令微调后大语言模型在特定场景下的幻觉问题。定量实验与临床医师的专业评估验证了本系统的有效性。