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

Authors

  • Martins Fideles dos Santos Neto Instituto Nacional de Câncer (INCA), Coordenação de Ensino (Coens). Rio de Janeiro (RJ), Brasil. https://orcid.org/0000-0003-2996-2222
  • Carina Munhoz de Lima Instituto Nacional de Câncer (INCA), , Coordenação de Ensino (Coens), Revista Brasileira de Cancerologia (RBC). Rio de Janeiro (RJ), Brasil. Universidade Federal Fluminense (UFF), Pós-Graduação em Ciência da Informação (PPGCI). Niterói (RJ), Brasil. https://orcid.org/0000-0002-1615-9177
  • Camila Belo Tavares Ferreira Instituto Nacional de Câncer (INCA), Coordenação de Ensino (Coens). Rio de Janeiro (RJ), Brasil. https://orcid.org/0000-0002-1423-513X
  • Robson Dias Martins Instituto Nacional de Câncer (INCA), Coordenação de Ensino (Coens). Rio de Janeiro (RJ), Brasil. https://orcid.org/0000-0002-5825-9408
  • Moreno Muniz Euzébio Rodrigues da Silva Instituto Nacional de Câncer (INCA), Coordenação de Ensino (Coens). Rio de Janeiro (RJ), Brasil. https://orcid.org/0009-0008-6738-018X
  • Kesya Cristina Silva de Paula Instituto Nacional de Câncer (INCA), Coordenação de Ensino (Coens). Rio de Janeiro (RJ), Brasil. https://orcid.org/0000-0001-7342-1971
  • Telma de Almeida Souza Instituto Nacional de Câncer (INCA), Coordenação de Ensino (Coens). Rio de Janeiro (RJ), Brasil. https://orcid.org/0000-0003-2786-1890
  • Paulo Roberto de Jesus Dantas Instituto Nacional de Câncer (INCA), Coordenação de Ensino (Coens). Rio de Janeiro (RJ), Brasil. https://orcid.org/0009-0007-6606-097X
  • Ricardo Vela de Britto Pereira Universidade Estadual do Rio de Janeiro (Uerj), Instituto de Matemática e Estatística (IME). Rio de Janeiro (RJ), Brasil. https://orcid.org/0000-0003-1473-2889
  • Alessandra de Sá Earp Siqueira Instituto Nacional de Câncer (INCA), Coordenação de Ensino (Coens). Rio de Janeiro (RJ), Brasil. https://orcid.org/0000-0003-3852-7580

DOI:

https://doi.org/10.32635/2176-9745.RBC.2026v72n2.5614

Keywords:

Artificial Intelligence, Bibliometrics, Big Data, Scientific Publication Indicators, Science, Technology and Society

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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Published

2026-03-12

How to Cite

1.
Santos Neto MF dos, Lima CM de, Ferreira CBT, Martins RD, Silva MMER da, Paula KCS de, Souza T de A, Dantas PR de J, Pereira RV de B, Siqueira A de SE. Artificial Intelligence in Cancer Control: A Global Bibliometric Analysis of Trends, Applications, and Implementation Challenges. Rev. Bras. Cancerol. [Internet]. 2026 Mar. 12 [cited 2026 Mar. 12];72(2):e-165614. Available from: https://rbc.inca.gov.br/index.php/revista/article/view/5614

Issue

Vol. 72 No. 2 (2026): Apr./May/June

Section

ORIGINAL ARTICLE

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