Leveraging AI for Real-Time Depression Detection in Healthcare Systems; a Systematic Review

Abstract

Pursuant to this rationale, this paper examines the possibilities of AI application in mental health assessment with special reference to proactive monitoring of depression among health-care systems. Depression is still one of the most widespread and most serious mental health disorders in the modern society. Routine assessment techniques are critical, but they can be conventional, and so it, diagnosis of depression may not be made when it is still in its early stage. Thus, for the first time, AI provides a chance to overcome this trend by allowing constant monitoring and early detection in practice. This study examines using AI approaches, including machine learning algorithms, natural language processing, and sentiment analysis to improve depression detection, treatment recommendations and managing chronic care. The paper also presents the limitations of AI implementation in the context of health care, potential ethical concerns, data protection problems, and integration problems. Last but not the least, envisagement’s of how the future AI is going to transform the mental health care sector are presented in the last section.