AI in Addiction: A Social Work-Informed Approach from Risk Assessment to Intervention

This study examines addiction via biopsychosocial and structural determinants and, from a social work perspective, positions AI-supported tools across risk assessment, monitoring/early warning, and psychosocial intervention. It is a conceptual review of empirical and theoretical studies (2010–2025) on addiction, AI, and decision-support systems. The reviewed literature indicates that AI-supported tools may help combine clinical and social data to estimate risk and relapse, identify vulnerable groups through social media content and digital footprints, enable monitoring and alerts via wearables and mobile apps, personalize interventions through chatbots and digital therapeutics, and strengthen case management through hybrid models. Yet structural risks also emerge, including privacy and dynamic consent concerns, algorithmic bias and stigma, covert surveillance, the digital divide, limited generalizability from small samples, and the relative neglect of behavioral addictions.