AI-Powered Mental Health Digital Twins

Abstract

Mental health disorders rank among the most challenging issues for global health and are largely responsible for the following: their increased prevalence, limited availability of care, high levels of stigmatization, and lack of objective biomarkers for both diagnosis and treatment monitoring. Conventional psychiatric practice is mainly based on subjective and episodic evaluations, which most of the time lead to late diagnosis, trial, and, error treatment, and overall poor long, term prognosis. To this end, Mental Health Digital Twins (MHDTs) can be seen as a revolutionary way to achieve precision psychiatry.MHDT is an ever, changing virtual representation of a particular patient which includes various data (behavioral patterns, physiological signals, clinical history, environmental context) integrated by AI and computational models to always reflect the most recent mental state of an individual.This paper provides an in, depth review of AI, driven mental health digital twins and traces the evolution of digital twin technology from the industrial sector to psychiatry. It argues the use of the technologies leading to the development of MHDTs such as wearable sensors, smartphones and digital phenotyping, cloud and edge computing, big data analytics, and advanced machine learning models.The major clinical applications being covered are the following: the early detection of mental disorders, personalized treatment planning, relapse prediction and prevention, continuous remote monitoring, digital self, management support, population, level mental health modeling, as well as research and drug development. real, world case studies illustrate current progress and practical.