Artificial intelligence in tobacco control: A systematic scoping review of applications, challenges, and ethical implications

Background

Tobacco use remains a significant global health challenge, contributing substantially to preventable morbidity and mortality. Despite established interventions, outcomes vary due to scalability issues, resource constraints, and limited reach.

Objective

To systematically explore current artificial intelligence (AI) applications within tobacco control, highlighting their usefulness, benefits, limitations, and ethical implications.

Method

This scoping review followed the Arksey and O’Malley framework and PRISMA-ScR guidelines. Five major databases (PubMed, Scopus, Web of Science, IEEE Xplore, and PsycINFO) were searched for articles published between January 2010 and March 2025. From 1,172 initial records, 57 studies met inclusion criteria after screening.

Results

AI-driven tools, including machine learning and natural language processing, effectively monitor social media for emerging tobacco trends and personalize smoking cessation interventions. Applications were predominantly focused on predictive modelling (using algorithms like XGBoost and SVM to predict e-cigarette use and relapse risk), cessation support (employing chatbots and reinforcement learning to improve accessibility), and social media surveillance (detecting synthetic nicotine promotions and analysing vaping trends). Approximately 22% of studies aligned with WHO FCTC Article 13 (tobacco advertising regulation), while 45% supported Article 14 (cessation services). However, tobacco industry interference remains a critical challenge, with AI technologies exploited to undermine public health initiatives, target vulnerable populations, and manipulate policy discussions. Critical issues including algorithmic bias, privacy concerns, interpretability challenges, and data quality must be addressed to ensure positive impact.

Conclusion

AI holds considerable promise for extending tobacco control if implemented ethically, transparently, and collaboratively. Future directions emphasize explainable AI development, integration of real-time intervention systems, global data inclusion, and robust cross-sector collaboration. While the current landscape shows a laudable start, it reflects the need for more diverse skill sets to fully harness AI’s extensive prospects for tobacco control and achieving tobacco endgame goals.