Applications Of Artificial Intelligence In Cognitive Behavioral Therapy: A Scoping Review - Info and Reading Options

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Title: Applications of Artificial Intelligence in Cognitive Behavioral Therapy: A Scoping Review Protocol Description: This scoping review aims to systematically map the existing literature on the integration of artificial intelligence (AI) technologies into cognitive behavioral therapy (CBT), with a focus on identifying methodologies, clinical applications, and emerging challenges. As digital mental health interventions rapidly evolve, AI-driven tools (e.g., chatbots, predictive analytics, natural language processing) are increasingly being explored to enhance the accessibility, personalization, and efficacy of CBT. However, the current evidence remains fragmented, and a comprehensive synthesis is needed to guide future research and clinical implementation. Objectives: To catalog the types of AI technologies applied in CBT delivery and research. To characterize the clinical contexts (e.g., depression, anxiety disorders) and populations where AI-enhanced CBT has been tested. To analyze methodological approaches (e.g., algorithm design, outcome measurement) and ethical considerations reported in existing studies. To identify gaps in evidence and propose directions for future innovation. Methods: Guided by the Arksey & O’Malley framework and PRISMA-ScR guidelines, this review will systematically search electronic databases (PubMed, IEEE Xplore, PsycINFO, and ACM Digital Library) for peer-reviewed articles published between 2010 and 2024. Grey literature and preprints will be excluded to ensure quality consistency. Two independent reviewers will screen titles/abstracts, followed by full-text review. Data extraction will focus on: AI technology type (e.g., machine learning, NLP, robotics). CBT delivery mode (e.g., app-based, hybrid human-AI interaction). Clinical outcomes (efficacy, engagement metrics, cost-effectiveness). Ethical/technical challenges (e.g., data privacy, algorithmic bias). Expected Outcomes: The synthesis will generate a taxonomy of AI applications in CBT, highlighting trends (e.g., shift from rule-based chatbots to adaptive neural networks) and critical barriers (e.g., lack of randomized controlled trials). Results will inform best practices for AI-CBT integration and prioritize research areas such as real-time emotion detection and culturally tailored interventions. Significance: By bridging the gap between AI innovation and mental health practice, this review seeks to empower clinicians, researchers, and policymakers to leverage AI responsibly, ultimately improving patient access to evidence-based CBT in diverse settings.

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