Artificial intelligence research on triple negative breast cancer has grown sharply over the past decade, with publication volume reaching an inflection point of upward growth in 2022 and peaking at 104 articles in 2025, according to a new bibliometric analysis published in Breast Cancer Research. The study, which mapped the global research landscape between 2015 and 2025, included 320 articles retrieved from the Web of Science Core Collection.
The analysis, led by Lan Chen, Zhaohao Zhang, Haihua Yang, Deyou Tao and Suna Zhou, was published on 21 September 2026 as an open access research article. The team used VOSviewer, CiteSpace and Tableau to carry out bibliometric and visualization analysis, examining the contribution characteristics of countries, institutions and authors, as well as co-occurrence patterns of keywords across the literature.
China and the United States emerged as the primary global contributors to research output in this field, the study found. At the institutional level, Fudan University and the MD Anderson Cancer Center were identified as the core high-output institutions, reflecting the concentration of artificial intelligence and breast cancer research expertise in a small number of leading centers.
Alongside the rising publication count, the researchers observed a simultaneous acceleration in annual citations, suggesting that the accumulated body of work is drawing increasing scholarly attention. The analysis of highly cited references was used to evaluate important achievements accumulated in the research field over the medium to long term, providing a retrospective view of which contributions have shaped the field’s direction.
Perhaps the most striking finding concerns how the scientific focus has shifted over the ten-year window. The research emphasis has moved from auxiliary diagnosis and molecular subtyping, including the Lehmann classification system, toward prediction of pathological complete response following neoadjuvant chemotherapy.
Current research trends, according to the mapping, are characterized by the integration of artificial intelligence with single-cell sequencing.
The bibliometric data show that this interest has translated into a rapidly expanding literature, with the 2025 peak of 104 articles marking the highest annual output in the study period.
The study’s conclusion offers a cautionary note alongside the growth figures. The authors suggest a potential disconnect between algorithm development and clinical implementation, and they point to multi-center validation and global data sharing as critical prerequisites for achieving the practical translation of this technology.
As a bibliometric analysis, the study describes the shape and trajectory of the research literature rather than reporting new clinical results. Its findings are drawn from 320 articles indexed in the Web of Science Core Collection between 2015 and 2025, and the authors present the work as a scientific basis and strategic pathway for the advancement of precision oncology in triple negative breast cancer. The article was published under open access terms, and the version shared at publication is subject to further editorial edits ahead of the final Version of Record.
For researchers and clinicians tracking the field, the mapping identifies where the literature has been and where it appears to be heading: from diagnostic assistance and molecular subtyping toward an integrated system for precision diagnosis and treatment, with single-cell sequencing and tumor microenvironment analysis as the emerging frontier.
Subject of Research: A bibliometric analysis of global research trends in artificial intelligence applied to triple negative breast cancer from 2015 to 2025.
Article Title: Global research trends and knowledge mapping of artificial intelligence in triple negative breast cancer: a ten-year bibliometric analysis
Article References: Chen, L., Zhang, Z., Yang, H., Tao, D., & Zhou, S. (2026). Global research trends and knowledge mapping of artificial intelligence in triple negative breast cancer: a ten-year bibliometric analysis. Breast Cancer Research. https://doi.org/10.1186/s13058-026-02392-8
Image Credits: AI Generated
DOI: 10.1186/s13058-026-02392-8
Keywords: artificial intelligence, triple negative breast cancer, bibliometric analysis, precision oncology, neoadjuvant chemotherapy, pathological complete response, molecular subtyping, single-cell sequencing, tumor microenvironment, Web of Science, VOSviewer, CiteSpace
News Source: Nathaniel Bowman. (October 29, 2026). AI in Triple Negative Breast Cancer Research Surges, Ten-Year Mapping Shows. Scienmag.
