ArticleJournal of the National Cancer Institute2019
Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.
Article in Journal of the National Cancer Institute, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 264 papers, 7 of them syntheses 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.
New Strategies Based on Artificial Intelligence in Breast Cancer Screening Programs in Córdoba With Digital Mammography and Digital Breast Tomosynthesis. A Prospective Evaluation.
The Use of AI as a Third Reader and During Consensus in a Double Reading Breast Cancer Screening Program in Sweden
Who cites it
264 citing papers in PubMed, 7 syntheses or guidelines pooled it, 651 citations in OpenAlex.
- ESR Essentials: artificial intelligence in breast imaging-practice recommendations by the European Society of Breast Imaging.European radiology · 2026Guideline
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in mammography: a systematic review of the external validation.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2024Pooled it
- Pooled it
- Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis.European radiology · 2022Pooled it
- Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review.Journal of the American College of Radiology : JACR · 2022Pooled it
- Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis.Radiology · 2022Pooled it
- Triaging mammography with artificial intelligence: an implementation study.Breast cancer research and treatment · 2025Trial
- Optimizing AI-assisted decision-making in mammography screening: focusing on uncertainties.Insights into imaging · 2026Article
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- Ten Years of Artificial Intelligence in Screening Mammography: A Systematic Review and Meta-Analysis of Diagnostic Accuracy and Clinical Implementation (Literature Published 2015-2025).Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Long-term prognostic implications of AI-detected versus AI-undetected breast cancers on mammography: a propensity score-matched analysis.European radiology · 2026Article
- The role of artificial intelligence in precision medicine for breast cancer.Discover oncology · 2026Review
- MuSTAF: Clinically Relevant Multi-task Spatiotemporal Attention Fusion Framework for Breast Cancer Detection with Longitudinal Mammography.medRxiv : the preprint server for health sciences · 2026Article
- Hybrid metaheuristic feature selection for breast cancer detection in digital mammography: a radiomics and deep learning pilot feasibility study.BMC medical imaging · 2026Article
- Enhancing Early Detection of Contralateral Breast Cancer in Breast Cancer Survivors Using AI-Assisted Mammography.Annals of surgical oncology · 2026Article
- AI-CAD for diagnostic mammography: comparison to radiologists according to different indications.European radiology · 2026Article
- Radiologist burnout: AI's true black box.European radiology · 2026Review
- SCAR-Net-assisted ultrasound diagnosis of postoperative scars and recurrent lesions in breast cancer.iScience · 2026Article
204 more citing papers are in PubMed but not listed here.
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM.
methodsNine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05.
resultsThe performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists.
conclusionsThe evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation.
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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.