Artificial intelligence in breast ultrasound: a systematic review of research advances

TL;DR

This study reviews how AI is being used in breast ultrasound to improve breast cancer diagnosis and highlights key trends and challenges.

Contribution

A systematic bibliometric analysis of AI-integrated breast ultrasound research from 2004-2025, identifying trends and collaboration patterns.

Findings

Annual AI-ultrasound publications increased significantly since 2024.

Deep learning emerged as the most prominent research theme after 2020.

The U.S. led in academic influence with 485 articles and 15,394 citations.

Abstract

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.

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.

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.

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