AI-Driven Solutions for Mental Health: Addressing the Global Mental Health Crisis

Abstract

The problems of mental health remain acute on the global level, and they cause enormous social and economic costs, thus requiring searching for new effective and efficient approaches. This paper explores using artificial intelligence (AI) in mental health, emphasizing the implementation of NLP, biometrics, and sentiment analysis for early detection and treatment. Using data from public datasets and cases, this study shows how AI can be used to diagnose and treat mental disorders. NLP models reached a precision and recall of over 80% in the detection of depression, while biometric systems had a high accuracy in detecting anxiety and stress. Further, the sentiment analysis using BERT provided F1 scores of more than 90 percent. However, some of the problems, like data privacy and its application in clinical practice, have not been solved. This research focuses on the opportunities for AI approaches in delivering affordable and accessible mental health services to shift to a more dual approach of AI-assisted tools and human professionals to tackle the existing gaps.