SynthesisFrontiers in oncology2025
Artificial intelligence in breast ultrasound: a systematic review of research advances.
Synthesis in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainable artificial intelligence in medical ultrasound: a WoSCC-based bibliometric analysis, evidence map, and Scopus concordance assessment.Frontiers in medicine · 2026Pooled it
- Hybrid metaheuristic feature selection for breast cancer detection in digital mammography: a radiomics and deep learning pilot feasibility study.BMC medical imaging · 2026Article
- Breast Ultrasound AI Under Dataset Shift: A Patient-Leakage-Aware Benchmark.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. Conclusion: 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.