Artificial Intelligence in Cancer Control: A Global Bibliometric Analysis of Trends, Applications, and Implementation Challenges

Abstract

Introduction: Artificial intelligence (AI) has been increasingly applied across the cancer control continuum, encompassing prevention, early detection, diagnosis, treatment, and health system management. The rapid growth of AI research in oncology reflects methodological diversity and global interest, with variability in implementation and validation contexts. Objective: To map the global scientific output on AI applications in cancer prevention and control, identifying publication trends, geographic distribution, study designs, types of AI, levels of implementation, and implications for health systems and public policies. Method: A bibliometric review following Donthu et al. Searches were conducted in six databases in April 2025, guided by PRISMA principles. Screening and eligibility were independently performed by two reviewers, with a third reviewer for adjudication. Variables: year, authorship/affiliation, journals, countries, keywords, study design, cancer type, control phase, AI type, level of implementation, cost-effectiveness, and Big Data. Analyses were conducted using Excel, VOSviewer, and EndNote. Results: Of 482 records, 134 studies were included. Publications increased after 2021, particularly in high-income countries (United States and China). Machine learning and deep learning predominated. Approximately one-third reported real-world clinical application; most studies were observational, narrative, or modeling-based. Few addressed cost-effectiveness or large-scale Big Data. Conclusion: There is sustained growth and diversification of AI in cancer control, with emphasis on diagnosis and treatment, heterogeneous levels of implementation, and relevance for health system organization and policy, including in low-resource settings.

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