ArticleTobacco induced diseases2026
Artificial intelligence and tobacco use: A bibliometric analysis 1997-2026.
Article in Tobacco induced diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionGlobally, tobacco use is a key public health issue. Exploring the impact of developing AI technologies on tobacco use and intervention will help promote the digital transformation of public health research. This study aims to map the trends, networks, and core themes of research at the intersection of artificial intelligence and tobacco use through visual analytics.
methodsIn this study, a bibliometric analysis was conducted on 335 articles on AI and tobacco use in the Web of Science core collection from 1997 to 2026. The search cutoff date was 8 April 2026. Descriptive and visual analysis were conducted on the publication trend, cooperation network, keyword co-occurrence, clustering, and burst of this literature.
resultsThe speed of publication of literature in this field has increased significantly since 2018. In the cooperation network, national cooperation is dominated by the United States. Universities, rather than transnational institutions, significantly promote cooperation. The authors' cooperation network is more dispersed. The characteristics of interdisciplinary cooperation are obvious. The research hotspots focus on several aspects. These include machine learning prediction of smoking, dialogue AI and robot interventions, and NLP and social media monitoring. Other topics encompass the analysis of smoking-cessation behavior among youth groups, combined with AI, deep learning, and behavioral analysis; and the evaluation of tobacco characteristics, tobacco products, media, and health. Keyword hotspots and bursts reveal that the field has recently paid special attention to the intervention of cutting-edge technologies in smoking and smoking cessation behavior.
conclusionsThrough data mining and visualization technology, this study reveals the overall evolution, interaction mode, and key fields of AI and tobacco use knowledge. The results provide an important framework for further research.
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Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.