AI in addiction: Harnessing technology for diagnosis, prevention, and recovery: A narrative review

Background: Addiction, both substance use and behavioral disorders, remains a significant global public health issue, affecting individuals, families, and societies. Traditional methods of addiction management struggle to meet the increasing demand for effective solutions. Artificial intelligence (AI) has emerged as a promising tool to address this challenge, offering innovative solutions across diagnosis, prevention, and recovery. Objective: To explore and synthesize the current applications of AI in addiction care, evaluating its potential in diagnosis, risk assessment, prevention, and recovery, while addressing associated challenges and ethical considerations. Methods: This narrative review draws from a range of studies, focusing on AI-driven technologies such as machine learning models, neuroimaging biomarkers, and wearable devices. The review discusses AI’s role in identifying addiction risk, diagnosing disorders, predicting treatment outcomes, and providing continuous recovery support. Results: AI has demonstrated significant effectiveness in addiction care, with machine learning algorithms achieving high diagnostic accuracy in substance use and behavioral disorders. Predictive analytics have shown promise in identifying at-risk populations and facilitating early intervention, while wearable devices and mobile applications support recovery by tracking physiological and behavioral indicators. Despite these advancements, challenges such as data privacy concerns, algorithmic bias, scalability in low-resource settings, and ethical considerations remain significant barriers. Conclusions: AI holds transformative potential in revolutionizing addiction care by enabling earlier detection, personalized interventions, and continuous monitoring of recovery. However, for AI to be successfully integrated into addiction care, addressing challenges such as data security, bias, and accessibility is essential. Future research should focus on expanding datasets, conducting longitudinal studies, and establishing ethical frameworks to guide AI adoption in this field.