Artificial intelligence in tobacco cessation: Trends, tools, and gaps – A scoping review

Background: AI promises tailored tobacco cessation interventions but landscape is heterogeneous. Methods: Scoping review of PubMed, Scopus, Web of Science, Google Scholar (2000–April 2025) per Arksey & O’Malley framework. Included ML, NLP, chatbots, predictive models and apps. Results: Most work from high-income countries; common uses — quit prediction, relapse risk, chatbots, automated messaging. Limited evidence on integration into clinical settings, LMIC applicability, equity and privacy reporting. Conclusion: AI holds potential in cessation but requires robust effectiveness, equity and implementation research, especially in LMICs.