Read, Diagnose and Chat: Towards Explainable and Interactive
  LLMs-Augmented Depression Detection in Social Media
Wei Qin Zetong Chen Lei Wang Yunshi Lan Weijieying Ren Richang Hong
This paper proposes a new depression detection system based on LLMs that is
both interpretable and interactive. It not only provides a diagnosis, but also
diagnostic evidence and personalized recommendations based on natural language
dialogue with the user. We address challenges such as the processing of large
amounts of text and integrate professional diagnostic criteria. Our system
outperforms traditional methods across various settings and is demonstrated
through case studies.