AI-Driven Personalized Cognitive Behavioral Therapy: Future Potential and Risks

Abstract

Background: Cognitive Behavioral Therapy (CBT) is a gold-standard intervention for depression, anxiety, and related disorders. With the growth of digital mental healthcare, Artificial Intelligence (AI) has emerged as a transformative force, enabling personalization, real-time monitoring, and scalability. AI-driven CBT tools, including chatbots, natural language processing, and machine learning models, are now capable of tailoring interventions dynamically, reducing dropout rates, and expanding access to underserved populations. Methodology: This review consolidates findings from randomized controlled trials, metaanalyses, implementation studies, and economic evaluations of AIenabled CBT platforms. It examines technological modalities such as conversational AI, predictive machine learning, and adaptive generative AI tools. Emphasis is placed on clinical efficacy, engagement metrics, safety considerations, ethical challenges, and scalability across diverse populations. Results: Evidence demonstrates that AI-driven CBT achieves moderate-to-high symptom reduction in depression and anxiety, with improved adherence compared to static digital interventions. Platforms like Woebot and Wysa show reduced dropout rates and higher therapeutic alliance through personalized interactions. Machine learning enhances risk stratification and symptom prediction, while economic models reveal cost-effectiveness in public health systems. Nonetheless, key risks persist, including data privacy concerns, algorithmic bias, reduced human connection, and clinical safety issues requiring rigorous oversight. Conclusion: AI-driven personalized CBT has strong potential to revolutionize mental healthcare by improving access, scalability, and treatment personalization while lowering costs. However, ethical safeguards, cultural sensitivity, and hybrid clinician-AI models are essential to balance automation with human empathy. Future development should focus on explainable AI, equity-driven design, and robust clinical validation to ensure safe and effective adoption.