DEVELOPING AI MODELS FOR REAL-TIME MONITORING OF MENTAL HEALTH CONDITIONS: UNDERSTANDING DEPRESSION, ANXIETY, AND STRESS THROUGH DIGITAL HEALTH TOOLS

Abstract

Mental health conditions including, but not limited to, depression, anxiety, and stress, have now become a major public health concern affecting millions of people worldwide. Traditionally, the diagnosis and treatment of these conditions depended heavily on self-reported scales and clinical interviews that often resulted in delayed interventions, misdiagnosis, and inconsistent treatments. The emergence of artificial intelligence (AI) and digital health tools brings the potential to change this narrative and facilitate real-time monitoring, early detection, and tailored intervention. AI innovations in mental health care range from machine learning (ML) algorithms and natural language processing (NLP) to wearable applications and therapy supported by chatbots.This paper discusses how AI technologies play an important role in predicting, assessing, and engaging patients continuously in the management of mental health. Chatbots help to provide conversational assistance for preliminary diagnoses. NLP analyzes dialogue patterns for detecting mood changes. Wearable devices measure physiological parameters, change in heart rate variability, and sleep patterns as objective measures of mental health. Predictive modeling furthers suicide prevention strategies by analyzing social media interaction behavior and real-time patient data. Nevertheless, challenges remain, including concerns around data privacy and algorithmic bias and regulatory compliance. This paper presents an extensive overview of AI initiatives in mental health and discusses the strengths, ethical considerations, and future directions. In this process, XAI principles, regulatory guidelines, and interdisciplinary collaboration need to be integrated into an operational framework to maximize the benefits of AI-driven solutions for mental health. Future studies should concentrate on enhancing the transparency of models, increasing integration with telehealth and carrying out validation studies in real-world settings on a large scale. When applied responsibly, AI could transform mental health care, enhancing accessibility, early intervention, and proffering personalized treatment outcomes.