AI-driven digital twin framework for personalized mental health monitoring and intervention

A growing global mental health crisis encounters ongoing obstacles due to discriminatory attitudes and spatial needs and rising treatment expenses. This study develops an innovative dialogue platform that offers personalized mental health assessments alongside prescribing specific virtual care recommendations according to real-time identified severity levels. Through Digital Twin technology a virtual mental state model updates and analyses patient data to generate tailored care experiences. Through a precise AI chatbot developed in collaboration with clinical psychopathologists our system operates as an efficient mental health symptom measurement tool. The BERT-based approach trained specifically on E-DAIC data delivers depression and other mental distress level identification features and classification functionality. The system employed NLP technology to provide feedback about individual psychological state during user dialogues which generated directed guidance. Our system underwent extensive testing that demonstrated 85% classification accuracy surpassing conventional methods. User tests validated the system interface model through a satisfaction score of 90% from satisfied participants. Research results validate that AI-driven mental health assessments assess psychological states accurately while delivering accessible reliable results as part of emotional support while eliminating conventional barriers to treatment. Digital twins revolutionize mental healthcare through their ability to develop stigma-free services in a new digital age where scalability and affordable treatment become possible.