ArticleNature communications2021
Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers, 4 of them syntheses that pooled it.
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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.
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Who cites it
125 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Explainable artificial intelligence in medical ultrasound: a WoSCC-based bibliometric analysis, evidence map, and Scopus concordance assessment.Frontiers in medicine · 2026Pooled it
- Revolution or routine? Comparing AI and traditional imaging in thoracic surgery outcomes: a systematic review.Journal of medicine and life · 2025Pooled it
- Artificial intelligence in bronchoscopy: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review.BMJ health & care informatics · 2024Pooled it
- Deep Learning Based on Automated Breast Volume Scanner Images for the Diagnosis of Breast Lesions: A Multicenter Diagnostic Study.International journal of medical sciences · 2025Trial
- Multimodal large language models for bladder tumor detection in cystoscopy: a retrospective benchmarking study.World journal of urology · 2026Article
- Artificial intelligence-based multimodal integration of ultrasound and digital breast tomosynthesis for breast-level risk classification.Precision clinical medicine · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Intrasystem Repeatability of S-Detect for Breast Ultrasound Classification With Identical Static Images: Single-Center Retrospective Repeatability Study.JMIR medical informatics · 2026Article
- Development and validation of an interpretable ultrasound radiomics model for benign and malignant classification of breast lesions: a multicenter large-sample study.Insights into imaging · 2026Article
- Five-Year Breast Cancer Risk Prediction From Screening Breast Ultrasound Using Deep Learning.medRxiv : the preprint server for health sciences · 2026Article
- An interpretable ultrasound-based deep learning system for early breast cancer in a Chinese population.Insights into imaging · 2026Article
- A non-invasive end-to-end intelligent assistance system for breast ultrasound.Nature communications · 2026Article
- A Deep Learning Model Integrating Ultrasound Images and Multidimensional Clinical Information for Differentiating Benign and Malignant Non-Mass Lesions.Diagnostics (Basel, Switzerland) · 2026Article
- A foundation generative model for breast ultrasound image analysis.Nature biomedical engineering · 2026Article
- Breathprints for Breast Cancer: Evaluating a Non-Invasive Approach to BI-RADS 4 Risk Stratification in a Preliminary Study.Cancers · 2026Article
- The Current Research Landscape on Integrating Artificial Intelligence with Ultrasound Imaging for Cancer Diagnosis: A Dual-Database Bibliometric Study.Current medicinal chemistry · 2026Article
- Direct deep learning analysis of three-dimensional automated breast ultrasound videos with reading mode optimization for breast cancer diagnosis.Ultrasonography (Seoul, Korea) · 2026Article
- An interpretable multimodal machine learning model integrating transvaginal ultrasound and clinical features for differentiating benign from malignant endometrial thickening in postmenopausal women.Frontiers in oncology · 2026Article
- Latent class analysis of conventional ultrasound features: a novel approach to predicting non-response to neoadjuvant chemotherapy in breast cancer.Frontiers in oncology · 2026Article
65 more citing papers are in PubMed but not listed here.
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Authors and funding
29 authors.
Funding
Abstract
Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.
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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.