ReviewDiagnostics (Basel, Switzerland)2022
Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis-A Rapid Review.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
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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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The value of artificial intelligence in ultrasound imaging for predicting molecular subtypes of breast cancer: a meta-analysis.Frontiers in oncology · 2026Pooled it
- Article
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Ultrasound-based artificial intelligence for breast lesion classification.Frontiers in oncology · 2026Review
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- The Clinical Application of Artificial Intelligence in Breast Imaging: Current Insights, Challenges, and Future Directions.Journal of breast cancer · 2025Review
- Integration of AI lesion classification, age, and BI-RADS assessment to reduce benign biopsies on breast ultrasound.European radiology · 2025Article
- Artificial intelligence-enhanced handheld breast ultrasound for screening: A systematic review of diagnostic test accuracy.PLOS digital health · 2025Article
- Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study.Scientific reports · 2025Article
- AI-Based Ultrasound Nomogram for Differentiating Invasive from Non-Invasive Breast Cancer Masses.Cancers · 2025Article
- Review
- Improving breast ultrasonography education: the impact of AI-based decision support on the performance of non-specialist medical professionals.Ultrasonography (Seoul, Korea) · 2025Article
- Comparative Study of AI Modes in Ultrasound Diagnosis of Breast Lesions.Diagnostics (Basel, Switzerland) · 2025Article
- Automated Analysis of Ultrasound for the Diagnosis of Pneumothorax: A Systematic Review.Cureus · 2024Review
- Enhancing Breast Cancer Detection through Advanced AI-Driven Ultrasound Technology: A Comprehensive Evaluation of Vis-BUS.Diagnostics (Basel, Switzerland) · 2024Article
- New Frontiers in Breast Cancer Imaging: The Rise of AI.Bioengineering (Basel, Switzerland) · 2024Review
- Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review.Bioengineering (Basel, Switzerland) · 2024Review
- Ultrasound Image Analysis with Vision Transformers-Review.Diagnostics (Basel, Switzerland) · 2024Review
- Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance.Frontiers in oncology · 2024Article
- Patient perception of meander-like versus radial breast ultrasound.Ultrasound international open · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUltrasound (US) is a fundamental diagnostic tool in breast imaging. However, US remains an operator-dependent examination. Research into and the application of artificial intelligence (AI) in breast US are increasing. The aim of this rapid review was to assess the current development of US-based artificial intelligence in the field of breast cancer.
methodsTwo investigators with experience in medical research performed literature searching and data extraction on PubMed. The studies included in this rapid review evaluated the role of artificial intelligence concerning BC diagnosis, prognosis, molecular subtypes of breast cancer, axillary lymph node status, and the response to neoadjuvant chemotherapy. The mean values of sensitivity, specificity, and AUC were calculated for the main study categories with a meta-analytical approach.
resultsA total of 58 main studies, all published after 2017, were included. Only 9/58 studies were prospective (15.5%); 13/58 studies (22.4%) used an ML approach. The vast majority (77.6%) used DL systems. Most studies were conducted for the diagnosis or classification of BC (55.1%). At present, all the included studies showed that AI has excellent performance in breast cancer diagnosis, prognosis, and treatment strategy.
conclusionsUS-based AI has great potential and research value in the field of breast cancer diagnosis, treatment, and prognosis. More prospective and multicenter studies are needed to assess the potential impact of AI in breast ultrasound.
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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.