Abstract
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.