Evaluating the Effectiveness and Acceptability of a GPT-4o and RAG-Based Voice Chatbot for Depression Screening Using PHQ-9

This study aims to assess the feasibility and acceptability of a voice-based chatbot, powered by GPT-4o and Retrieval-Augmented Generation (RAG), for conducting depression screening using the Patient Health Questionnaire-9 (PHQ-9). The PHQ-9 is a validated self-report instrument widely used to screen, diagnose, and monitor the severity of depression. It consists of nine questions that correspond to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for major depressive disorder. Respondents rate the frequency of symptoms experienced over the past two weeks on a scale from 0 ("not at all") to 3 ("nearly every day"). The total score (ranging from 0 to 27) indicates the severity of depressive symptoms, categorized into minimal, mild, moderate, moderately severe, or severe depression. The PHQ-9 is also used to assess functional impairment and guide treatment decisions in clinical and research settings.

The voice-based chatbot integrates GPT-4o, with RAG to enhance its ability to provide informed and contextualized responses during interactions. GPT-4o serves as the conversational engine, capable of generating human-like, empathetic, and contextually appropriate dialogue. RAG, on the other hand, enables the chatbot to retrieve and incorporate external, up-to-date knowledge from a curated database or knowledge repository, ensuring the accuracy and reliability of its responses.