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

Abstract

Mental health issues such a stress, sadness and anxiety are becoming an epidemic of major proportions that ail millions of people around the world.  Clinical interviews with self-reported scales were in the past the key methods of diagnosing and treating these diseases.  This usually implied that treatment was delayed, allocation was erroneous and treatment not consistent.  AI and digital health solutions have the potential to transform this same story.  The tools could be used to monitor people in real time, identify issues as early as possible, and provide them care that is customized to their needs.  Ways in which AI is improving mental health care are through ML algorithms, NLP, wearable applications, and chatbots that may help with therapy.  This paper will discuss how AI technology plays a critical role in the on-going estimation, analysis, and engagements of those who receive mental health treatment.  Chatbots will assist in the chat aimed at helping individuals understand what ails them on the first time.  NLP is also interested in the way the individuals are talking to others to check whether mood change has occurred or not.  Wearable electronics has the objective of measuring mental wellbeing (by monitoring physiological indicators, heart variability rate and sleep trend).  Analysis of relationships between individuals through social media and predictive modelling as well as on-real-time data on patients can assist in preventing people committing suicide.  There are though certain issues that should be addressed, including the fear of data privacy, bias of algorithms, and obeying the rules.  The paper provides a thorough examination of the current state of AI usage and limitations in the field of mental health, and its benefits, the impact of ethical issues, and the future of it. We must combine the concepts of XAI, rules and cooperation across sectors into a single way of doing the business to maximize the potential of AI-based mental health treatment.  Rather than refining them so that they are better able to identify trends in the future, they should be made more transparent, e.g. through including offers of telehealth integration and they should be tried out in real world settings through extensive research.  The convenience aspect of mental health care can be improved by making it easier to access services by more individuals, quickening diagnosis and treatment, and providing an individual with a variety of treatment outcomes.