Early detection of depression and anxiety in the USA using generative AI

Abstract

Mental health disorders such as depression and anxiety affect millions of people in the United States. Early detection is critical for effective intervention and management. This paper proposes a novel framework leveraging Generative Artificial Intelligence (Gen AI) tools for the early detection of these mental health conditions. The system integrates natural language processing (NLP), sentiment analysis, and behavioral data analytics to identify early signs of depression and anxiety from digital footprints such as social media posts, wearable devices, and health records. A conceptual diagram is included to illustrate the proposed model.