Published Paper Details:

The intersection of artificial intelligence (AI) and mental health presents unprecedented opportunities to transform how mental illnesses are understood, diagnosed, and treated. This research explores the development of an AI-powered Cognitive Twin--a dynamic digital model that mirrors an individual's psychological and behavioral states using multi-modal data sources such as speech patterns, wearable sensor data, smartphone usage, and social media activity. By applying machine learning, natural language processing, and behavioral analytics, the Cognitive Twin can continuously learn from a person's digital footprint to detect early signs of anxiety, depression, and stress. The goal is to personalize mental health interventions by simulating various therapeutic strategies and predicting their outcomes for a specific individual before actual clinical implementation. The system also incorporates privacy-preserving AI techniques such as federated learning and differential privacy to ensure data security and ethical compliance. This research aims to support mental health professionals in making data-driven, individualized treatment plans, and to empower patients with real-time self-awareness tools. Ultimately, the project aspires to bridge the gap between traditional mental healthcare and intelligent, responsive digital therapeutics.