REVIEW article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
This article is part of the Research TopicHarnessing Machine Learning for Enhanced Biomedical Diagnosis and Early Disease Detection: Bridging Data Science and HealthcareView all 14 articles
Ultrasound-based Artificial Intelligence for Breast lesion classification
Provisionally accepted
- 1Huazhong University of Science and Technology Tongji Medical College Tongji Hospital, Wuhan, China
- 2The First Affiliated Hospital of Shihezi University Department of Ultrasound, Shihezi, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Breast cancer is the most prevalent cancer among women. Early and accurate screening is crucial for improving patient outcomes. Ultrasound is a valuable diagnostic tool, particularly for dense breasts, yet its efficacy can be limited by operator dependency and interpretive variability. Artificial intelligence (AI) has shown significant potential to enhance the accuracy and efficiency of breast ultrasound. However, translating AI from research to clinical practice remains challenging due to several persistent gaps: the lack of robust clinical validation for generative AI in image enhancement; insufficient focus on AI for diagnosing non-mass lesions, which constitute a notable proportion of malignancies; and limited multi-center effectiveness data for commercial computer-aided diagnosis systems. This narrative review synthesizes recent advancements in AI for breast ultrasound and provides a critical, multifaceted analysis that integrates technological evolution, clinical-translation challenges, and implementation frameworks. Importantly, it highlights pervasive methodological limitations, such as small sample sizes, retrospective single-center designs, and inadequate external validation, that often lead to overestimation of real-world AI performance. By offering both actionable insights and a cautionary perspective, this review aims to guide the rigorous, evidence-based translation of AI into clinically viable tools.
Keywords: artificial intelligence, Breast lesion, Convolutional Neural Networks, deep learning, ultrasound
Received: 02 Dec 2025; Accepted: 10 Feb 2026.
Copyright: © 2026 Ma, Wang, Dong, Cheng, Zhao and Cui. 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: Xin-Wu Cui
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.