Classifying Anxiety and Depression through LLMs Virtual Interactions: A Case Study with ChatGPT

Abstract

Mental health has long been studied, and many AI-based approaches have been proposed for diagnosis and adjunctive therapy. The emergence of Pre-trained Large Language Models (LLMs) has had a profound impact on various fields, but the potential of using ChatGPT for cognitive behavior therapy is largely unexplored. Therefore, there is an urgent need to build and design a virtual interactive framework for assisted diagnosis/treatment. In this paper, we present a virtual interaction framework based on LLMs that allows participants to engage in a dialogue with a virtual character, analyse mental health issues through augmented LLMs, and make suggestions during the dialogue to alleviate the psychological problems they are currently facing. Based on this framework, we develop a use case for the application of ChatGPT in the field of emotional disorders. Specifically, we use data from question-and-answer dialogues in real-life scenarios to populate the current exploration of ChatGPT’s potential for depression and anxiety detection. The case study shows the great potential of ChatGPT in the analysis of depression and anxiety tests. The feasibility of a virtual interaction framework based on LLMs has been preliminarily demonstrated.

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