AI for Mental Health: Innovations in Diagnosis and Therapy

Abstract

Objective: This review aims to explore the current and future trends in using artificial intelligence (AI) for diagnosing and treating mental health disorders. It emphasizes innovative diagnostic approaches and individualized therapy options made possible by AI technologies.

Method: A comprehensive literature review was conducted, focusing on articles published between January 2010 and December 2022. Databases such as PubMed, IEEE Xplore, ACM Digital Library, and Google Scholar were searched using relevant keywords. Inclusion criteria required original research articles published in English, involving AI applications in mental health diagnosis, screening, monitoring, or therapy. The selected studies were assessed for quality using established checklists, and data were extracted on study aims, participants, AI techniques, and outcomes.

Results: AI technologies, including machine learning algorithms, natural language processing (NLP), and speech analysis, have demonstrated significant potential in enhancing the accuracy of mental health diagnoses. For instance, AI systems have been able to distinguish between bipolar disorder and major depressive disorder with high accuracy. In treatment, AI-driven tools such as chatbots and mobile applications have shown efficacy in delivering cognitive behavioral therapy and other interventions. Wearable devices and virtual reality applications provide additional support by enabling continuous monitoring and exposure therapy.

Conclusion: AI has the potential to revolutionize mental health care by improving diagnostic precision, personalizing treatment, and providing continuous support. However, ethical considerations, such as data privacy and algorithmic bias, must be addressed to ensure the responsible use of AI in clinical settings. With careful integration and oversight, AI can significantly enhance the effectiveness and accessibility of mental health services, offering new hope for patients and clinicians alike.