Abstract
Background: Understanding the global landscape of artificial intelligence (AI) in breast cancer research is essential for guiding future studies. This bibliometric analysis offers a comprehensive overview of research published between January 2014 and November 1, 2024. Methods: We systematically searched the Web of Science Core Collection (SCI-Expanded) for relevant publications within the specified timeframe. Bibliometric network analyses, including keyword and country co-occurrence, were conducted using VOSviewer, while quantitative analyses of trends, impact, and contributors were performed using R. The data encompassed publications, citations, authors, institutions, countries, journals, and keywords. Results: The co-occurrence analysis of keywords identified six primary thematic clusters focused on "breast cancer," "deep learning," "machine learning," "artificial intelligence," "classification," and "transfer learning." China, India, and the USA were the most productive countries. Wang (China), Liu (China), and Zhang (USA) were the most prolific authors, while Helbich TH (Australia), Hipp JD (USA), and Khan S (India) were the most highly cited. Scientific Reports published the greatest number of articles, whereas PLOS ONE received the highest number of citations within this dataset. Conclusions: This study provides an objective overview of the global landscape of AI in breast cancer research from 2014 to November 1, 2024. Our findings map publication trends, influential authors, countries, journals, and key research themes. These findings offer a valuable reference for researchers, institutions, and journals to understand the current landscape, identify collaboration opportunities, emerging trends, knowledge gaps, and highlight promising areas for future research in this dynamic field.