What question did this study set out to answer?

The study aims to examine the role of artificial intelligence in tobacco cessation interventions and identify existing gaps in research.

March 28, 2026Open Access

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

Key Points

The study aims to examine the role of artificial intelligence in tobacco cessation interventions and identify existing gaps in research.
Conducted a scoping review using databases like PubMed and Scopus from 2000 to April 2025.
Applied Arksey & O’Malley framework for comprehensive mapping of the literature.
Included tools such as machine learning, natural language processing, and chatbots.
Most innovations are from high-income countries.
Common applications include quit prediction, relapse risk assessment, and chatbots for automated support.
There is limited evidence on the integration of these AI tools into clinical practice and their applicability in low- and middle-income countries.

Abstract

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 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.

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

Key Points

Abstract

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