The field of mental health research is witnessing a significant shift towards the development and integration of AI-powered tools for diagnosis, support, and treatment. Recent studies have focused on addressing the challenges associated with AI-mediated therapy, such as privacy concerns, over-disclosure of personal information, and the potential for bias and misinterpretation. Researchers are exploring innovative solutions, including the development of AI-literacy frameworks, behavioral health safety filters, and game-theoretic policy optimization methods. Additionally, there is a growing emphasis on creating actionable platforms for evaluating the performance of large language models in mental health contexts and integrating explainable AI techniques into clinical practice. Noteworthy papers in this area include: An AI-Based Behavioral Health Safety Filter and Dataset for Identifying Mental Health Crises in Text-Based Conversations, which demonstrated the effectiveness of the Verily behavioral health safety filter in detecting mental health crises. AI-Powered Early Diagnosis of Mental Health Disorders from Real-World Clinical Conversations, which achieved over 80% accuracy in diagnosing mental health disorders using machine learning models.