SYSTEMATIC REVIEW article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1619364
This article is part of the Research TopicAdvances in Oncological Imaging TechniquesView all 18 articles
Artificial Intelligence in Breast Ultrasound: A Systematic Review of Research Advances
Provisionally accepted
- 1Third Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China
- 2First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan Province, China
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Objective: Through bibliometric visualization analysis, this study aims to summarize research progress in artificial intelligence (AI)-integrated ultrasound technology for breast cancer, reveal research hotspots, development trends, and international collaboration patterns, thereby providing references for clinical diagnosis and therapeutic decision-making. Methods: Based on the Web of Science Core Collection (SCI-Expanded), we retrieved relevant literature from 2004-2025 (1,876 articles finally included). VOSviewer (v1.6.20), CiteSpace (v6.3.1 Basic), and Microsoft Excel 2019 were employed for visual analysis of publication volume, national/institutional collaboration, author networks, keywords, and co-citation relationships. Results: Annual publications have shown a progressive increase since 2024. The United States (485 articles, 15,394 total citations) demonstrated the highest academic influence. Core researchers included Moon Woo Kyung (38 articles), while Seoul National University Hospital (47 articles) emerged as a key collaborative institution. Keyword clustering identified "deep learning", "breast ultrasound", and "machine learning" as research hotspots, with burst detection analysis revealing "deep learning" as the most prominent emerging theme (post-2020 surge). Radiology ranked as the most cited journal (4,258 citations), with foundational works by Berg WA (2008) and Al-Dhabyani W (2020) constituting the highest-impact literature.AI-ultrasound integration is suggested to have potential for enhancing diagnostic accuracy in breast cancer, although global research still exhibits regional disparities. Future efforts should strengthen international collaboration, optimize deep learning-based imaging analysis, leverage big data for treatment optimization and prognosis prediction, while addressing technical challenges including data quality assurance and algorithm sharing mechanisms.
Keywords: breast cancer, ultrasound, artificial intelligence, VOSviewer, Citespace
Received: 28 Apr 2025; Accepted: 12 Sep 2025.
Copyright: © 2025 LIU, Pian, Chen, Zhao, Liu, Meng and Zeng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Linping Pian, First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan Province, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.