Bibliometric analysis of research on artificial İntelligence applications in breast cancer diagnosis.

Publisher: SAGE Publications Country of Publication: United States NLM ID: 9314590 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-7401 (Electronic) Linking ISSN: 09287329 NLM ISO Abbreviation: Technol Health Care Subsets: MEDLINE

Publication: 2025- : [Thousand Oaks, CA] : SAGE Publications
Original Publication: Amsterdam ; New York : Elsevier, c1993-

Breast Neoplasms*/diagnosis ; Bibliometrics* ; Artificial Intelligence*; Humans ; Female

ObjectiveThis analysis aims to examine studies on artificial intelligence (AI) applications in breast cancer diagnosis through bibliometric methods, focusing on temporal and geographical trends. It contributes to shaping the field's roadmap and helping researchers adapt to technological innovations.MethodA comprehensive search was conducted in the Web of Science (WOS) database. Bibliometric analyses of data from 2013-2024 were performed using VOSviewer and Bibliometrix R programs.ResultsThe analysis included 1537 articles. A significant rise in research activity was observed in 2019. The thematic analysis highlighted topics like histopathology, feature selection, deep learning, and machine learning. India was the most productive country with 405 studies. Keyword analysis showed increased usage of terms like transfer learning, CNN, and radiomics. U.S. was the most cited country with 7511 citations. Concept co-occurrence analysis revealed strong associations between terms such as feature selection, datasets, algorithm performance, and classification methods. Bejnordi's 2017 study was identified as the most influential, with 1909 citations.Discussion and ConclusionThis study identifies key authors, influential works, and trending topics, offering a broad understanding of the field's structure and evolution. It helps outline the advancements and emerging directions in AI applications for breast cancer diagnosis.

Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Keywords: artifical intelligent; bibliometric analysis; breast cancer; data mining; deep learning; machine learning

Date Created: 20250820 Date Completed: 20260202 Latest Revision: 20260205

20260205

PMC12864533

10.1177/09287329251362602

40831333