ArticleJournal of breast imaging2025
External Validation of a Commercial Artificial Intelligence Algorithm on a Diverse Population for Detection of False Negative Breast Cancers.
Article in Journal of breast imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning Algorithms Versus Radiologists in Digital Breast Tomosynthesis for Breast Cancer Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Hybrid Metaheuristic Feature Selection for Breast Cancer Detection in Digital Mammography: A Feasibility Study with Nested Validation, Benchmarking, and External Stress Testing.Journal of clinical medicine · 2026Article
- Application of Artificial Intelligence in Breast Ultrasound Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Mammographic classification of interval breast cancers and artificial intelligence performance.Journal of the National Cancer Institute · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveThere are limited data on the application of artificial intelligence (AI) on nonenriched, real-world screening mammograms. This work aims to evaluate the ability of AI to detect false negative cancers not detected at the time of screening when reviewed by the radiologist alone.
methodsA commercially available AI algorithm was retrospectively applied to patients undergoing screening full-field digital mammography (FFDM) or digital breast tomosynthesis (DBT) at a single institution from 2010 to 2019. Ground truth was established based on 1-year follow-up data. Descriptive statistics were performed with attention focused on AI detection of false negative cancers within these subsets.
resultsA total of 26 694 FFDM and 3183 DBT examinations were analyzed. Artificial intelligence was able to detect 7/13 false negative cancers (54%) in the FFDM cohort and 4/10 (40%) in the DBT cohort on the preceding screening mammogram that was interpreted as negative by the radiologist. Of these, 4 in the FFDM cohort and 4 in the DBT cohort were identified in breast densities of C or greater. False negative cancers detected by AI were predominantly luminal A invasive malignancies (9/11, 82%). Artificial intelligence was able to detect these false negative cancers a median time of 272 days sooner in the FFDM cohort and 248 days sooner in the DBT cohort compared to the radiologist.
conclusionArtificial intelligence was able to detect cancers at the time of screening that were missed by the radiologist. Prospective studies are needed to evaluate the synergy of AI and the radiologist in real-world settings, especially on DBT examinations.
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Registered trials
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