Bibliometric and Content Analysis of Artificial Intelligence in Smoking Cessation - Worldwide, 1982-2026
Aaron Wan Jia He1, Ferrina Hoi Yan Cheung1, Yuna Shao1
1School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China.
What Is Already Known About This Topic?:
Tobacco use remains a major public health challenge and the leading preventable cause of death worldwide. Artificial intelligence is increasingly applied in smoking cessation research, but its overall thematic landscape and development trends have not been systematically described.
What Is Added By This Report?:
This bibliometric and content analysis identified 1,728 publications on artificial intelligence (AI) in smoking cessation from inception to March 5, 2026. Research output increased markedly after 2017, with intervention as the dominant domain, while prediction and diagnosis emerged as closely related AI areas and monitoring remained substantially underexplored.
What Are The Implications For Public Health Practice?:
AI has strong potential to enhance tobacco control through digital interventions, predictive analytics, diagnostic support, and longitudinal monitoring. Expanding monitoring applications and strengthening trustworthy governance of AI may help improve smoking cessation strategies and support global tobacco control goals.
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