A Novel Model for Digital Twins in Mental Health: The Biopsychosocial AI-Driven Digital Twin (BADT) Framework

The digital twin technology (DTT) in mental health care is a revolutionary way to custom diagnostics and therapy through precision medicine. This paper describes the Biopsychosocial AI-Driven Digital Twin (BADT) Framework, a new model that unites artificial intelligence (AI) with biopsychosocial data to form virtual replicas of patients which are dynamic and real-time. The BADT framework merges biological, psychological, and social data streams, such as genetic markers, physiological measures, cognitive assessments, and environmental factors into a single system that evolves continuously along with the patient's condition. By means of AI and machine learning, the model gives predictive analytics for the early diagnosis, risk assessment, and personalized treatment plan simulation. The most important characteristics are the possibility for the real-time monitoring by the wearable device, the dynamic treatment planning, and the simulation of the therapy response. The BADT model stands for a critical move to personalized psychiatry, which clinicians by the help of powerful instruments for individualized, and data-driven decision-making as well as to the moral and ethical consideration of privacy and fairness issues. This framework not only promises to be more effective and responsive in mental healthcare but also carries the potential to enhance patient results by immediately identifying and preventing mental health crises through time-bound and personalized interventions.