An AI-Powered Mental Health Assistant for Personalized and Adaptive Care

The global mental health crisis highlights the urgent need for accessible, personalized mental health care. However, access to qualified therapists is often limited, especially in underserved regions. This paper introduces an AI-powered mental health assistant designed to address these challenges by providing adaptive, group-specific, and ethical support. Key innovations include a fine-tuned BERT model for detecting nuanced emotions and cognitive distortions, Graph Convolutional Networks (GCNs) to model emotional state transitions, and reinforcement learning (RL) to dynamically optimize therapeutic interventions. Emphasis is placed on ensuring ethical standards, including fairness and transparency, to create an inclusive and accessible tool. Evaluations suggest significant potential to provide transformative mental health care, especially for traditionally underserved populations.