ArticleRadiology. Artificial intelligence2024
A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography.
Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of artificial intelligence in breast cancer screening programmes: a systematic review.BMJ open · 2025Pooled it
- Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review.Lasers in medical science · 2026Review
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- A novel statistical framework for quantifying risks and benefits of AI automation in screening mammography.PLOS digital health · 2026Article
- From de-skilling to up-skilling: How artificial intelligence will augment the modern physician.Journal of experimental orthopaedics · 2026Review
- Over-detection and over-surveillance in breast screening: current status and the potential for artificial intelligence optimisation.Insights into imaging · 2025Review
- Generative AI-Enhanced Microcalcification Detection in Full-Field Digital Mammography: Reducing False Positives with High Sensitivity.Research square · 2025Article
- MRI-based artificial intelligence models for post-neoadjuvant surgery personalization in breast cancer: a narrative review of evidence from Western Pacific.The Lancet regional health. Western Pacific · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose To evaluate the ability of a semiautonomous artificial intelligence (AI) model to identify screening mammograms not suspicious for breast cancer and reduce the number of false-positive examinations. Materials and Methods The deep learning algorithm was trained using 123 248 two-dimensional digital mammograms (6161 cancers) and a retrospective study was performed on three nonoverlapping datasets of 14 831 screening mammography examinations (1026 cancers) from two U.S. institutions and one U.K. institution (2008-2017). The stand-alone performance of humans and AI was compared. Human plus AI performance was simulated to examine reductions in the cancer detection rate, number of examinations, false-positive callbacks, and benign biopsies. Metrics were adjusted to mimic the natural distribution of a screening population, and bootstrapped CIs and
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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.