Artificial Intelligence in Mental Health: Leveraging Machine Learning for Diagnosis, Therapy, and Emotional Well-being

Abstract

There is an unprecedented integration of machine learning and AI in mental healthcare which is creating changes in the ways diagnosis and treatment are provided ultimately improving the emotional health of the affected individuals. With the prevalence of mental health issues on the rise, the demand for new and improved approaches to the practice are evident. The purpose of the research is to analyze the effectiveness of AI technologies in the diagnosis of diseases, personalization of therapeutic programs and maintenance of psychological health. We performed a systematic analysis of available literature and case studies on the use of several algorithms of machine learning in clinical practice. We further examined these AI technologies and their effectiveness including chat bots and virtual therapy platforms in the delivery and performance in practice. We also carried out an online poll to solicit opinions regarding a computer application for two-way texting. AI-enhanced predictors of diagnosis and the like were associated with improved timeliness of diagnosis and increased accuracy of AI-enhanced therapy increased connectivity to patients significantly. AI-powered technologies further have been noted to have the potential in addressing emotional distress by making it easy and efficient to provide timely interventions and monitoring of mental health status. From this research, it is evident that there is more to the field of mental health practice today than what was not available before AI came along, it is not only the practice processes that have been enhanced by AI, but quite importantly, the psychological wellness of practitioners and their clientele. In the future work, emphasis should be placed on adding.