ENHANCING TREATMENT EFFICACY AND USER ENGAGEMENT IN MENTAL HEALTH CARE: INTEGRATING AI INTO TRADITIONAL THERAPEUTIC PRACTICES AND ADDRESSING ETHICAL CONSIDERATIONS

Abstract

This study examines the integration of artificial intelligence (AI) into mental health care, focusing on its impact on treatment efficacy, user engagement, and ethical considerations. As mental health issues rise globally, AI tools such as catboats and predictive analytics are emerging to complement traditional therapeutic practices by offering personalized support and real-time monitoring. The study employs a mixed-methods approach, utilizing quantitative assessments with a sample of 100 practitioners and 200 patients, combined with qualitative insights from semi-structured interviews. Data were collected through validated tools like the PHQ-9 and GAD-7 to measure treatment outcomes, and thematic analysis explored ethical concerns, including privacy and the authenticity of AI-driven therapeutic relationships. Results indicated significant improvements in depression and anxiety symptoms, with user engagement serving as a key mediator in enhancing treatment efficacy. Structural equation modeling (SEM) further highlighted the importance of engagement in translating AI interventions into meaningful clinical outcomes. However, the study is limited by its reliance on self-reported data and a geographically specific sample. These findings suggest that while AI has the potential to improve mental health outcomes, its success largely depends on fostering high levels of user engagement. Future research should explore diverse populations and investigate the long-term effects of AI in mental health care, focusing on how different AI tools perform across various mental health conditions. By understanding these dynamics, mental health practitioners can better integrate AI into their practices, ensuring ethical considerations are met while optimizing treatment outcomes. Keywords: AI, mental health, user engagement, treatment