Integration of Artificial Intelligence in Triple-Negative Breast Cancer Research: A Bibliometric and Emerging Trends Analysis

Abstract Triple-negative breast cancer (TNBC) is defined by the absence of estrogen and progesterone receptors and low expression of the human epidermal growth factor receptor 2 (HER2) protein, which limits the efficacy of available treatment strategies. Recent advances in data science have spurred the application of artificial intelligence (AI) in TNBC research, leading to notable progress. The purpose of this study was to evaluate the current landscape, primary research areas, and emerging trends in AI integration in TNBC research. The analysis aimed to provide a comprehensive overview of research progress and to identify future research directions. Using the Bibliometrix R package and VOSviewer, 461 documents indexed in the Scopus database from 2011 to 2025 were examined. Results indicate rapid expansion in this research field, with an annual growth rate of 38.02%. China and the United States of America emerged as the leading contributors, with the USA leading in global collaboration. The journal Cancers had the highest number of publications and the greatest impact in this field. The University of Texas MD Anderson Cancer Center was the most relevant affiliation, while Zhang J and Wang X emerged as the most productive and impactful authors, respectively. Biomarkers , radiomics , and feature selection were among the top emerging trends in this field. Identified future research directions include clinical translation of AI models, multi-omics for personalized therapy, non-invasive diagnostics, liquid biopsy, and the tumor microenvironment. Increased and sustained collaboration among authors is needed to shape the research landscape on AI integration into TNBC research.