ArticleEuropean radiology2019
Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study.
Article in European radiology, 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 97 papers, 4 of them syntheses that pooled it.
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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
97 citing papers in PubMed, 4 syntheses or guidelines pooled it, 210 citations in OpenAlex.
- How Does Medical Artificial Intelligence Revolutionize Physician Productivity?Yonsei medical journal · 2026Pooled it
- Performance of artificial intelligence in breast cancer screening programmes: a systematic review.BMJ open · 2025Pooled it
- Pooled it
- Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis.Radiology · 2022Pooled it
- Beyond detection: Does AI see prognosis in mammography?European radiology · 2026Article
- Artificial intelligence-assisted triage of screening mammograms following breast-conserving therapy: a comparative simulation study.La Radiologia medica · 2026Article
- Noninferiority and Efficiency/Revenue Facilitation (NERF) Endpoints : Shifting Grounds of Argument in Health AI Interventional Studies.Journal of bioethical inquiry · 2026Article
- When intelligence meets radiology: the dual impact of ai on radiologists' workload, burnout, and economic value.Abdominal radiology (New York) · 2026Review
- Comparison of artificial intelligence (AI) services for Breast Imaging-Reporting and Data System (BI-RADS) classification on mammograms.Quantitative imaging in medicine and surgery · 2026Article
- AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial.Nature medicine · 2026Article
- Clinical Performance Tradeoffs of ChatGPT-5.2 Thinking (OpenAI) Compared with Radiologist Interpretation in Biopsy-Referred Mammography: Cancer Detection, False Positives, and Laterality.Tomography (Ann Arbor, Mich.) · 2026Article
- Assessing the robustness of AI lesion risk scores at different exposure settings using an anthropomorphic breast phantom.Radiation protection dosimetry · 2026Article
- Adding artificial intelligence case malignancy scoring to reduce screen-reading workload in breast screening program: results of the retrospective REAI program.La Radiologia medica · 2026Article
- Combined use of two artificial intelligence-based algorithms for mammography triaging: a retrospective simulation study.European radiology · 2026Article
- A novel statistical framework for quantifying risks and benefits of AI automation in screening mammography.PLOS digital health · 2026Article
- Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology.World journal of methodology · 2025Review
- Early Results of Using AI in Mammography Screening for Breast Cancer.Journal of clinical medicine · 2025Article
- Robust evaluation of tissue-specific radiomic features for classifying breast tissue density grades.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- An Exploration of Discrepant Recalls Between AI and Human Readers of Malignant Lesions in Digital Mammography Screening.Diagnostics (Basel, Switzerland) · 2025Article
- Reducing the workload of medical diagnosis through artificial intelligence: A narrative review.Medicine · 2025Review
37 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
15 authors at 10 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload. METHODS AND MATERIALS: A total of 2652 DM exams (653 cancer) and interpretations by 101 radiologists were gathered from nine previously performed multi-reader multi-case receiver operating characteristic (MRMC ROC) studies. An AI system was used to obtain a score between 1 and 10 for each exam, representing the likelihood of cancer present. Using all AI scores between 1 and 9 as possible thresholds, the exams were divided into groups of low- and high likelihood of cancer present. It was assumed that, under the pre-selection scenario, only the high-likelihood group would be read by radiologists, while all low-likelihood exams would be reported as normal. The area under the reader-averaged ROC curve (AUC) was calculated for the original evaluations and for the pre-selection scenarios and compared using a non-inferiority hypothesis.
resultsSetting the low/high-likelihood threshold at an AI score of 5 (high likelihood > 5) results in a trade-off of approximately halving (- 47%) the workload to be read by radiologists while excluding 7% of true-positive exams. Using an AI score of 2 as threshold yields a workload reduction of 17% while only excluding 1% of true-positive exams. Pre-selection did not change the average AUC of radiologists (inferior 95% CI > - 0.05) for any threshold except at the extreme AI score of 9.
conclusionIt is possible to automatically pre-select exams using AI to significantly reduce the breast cancer screening reading workload. KEY POINTS: • There is potential to use artificial intelligence to automatically reduce the breast cancer screening reading workload by excluding exams with a low likelihood of cancer. • The exclusion of exams with the lowest likelihood of cancer in screening might not change radiologists' breast cancer detection performance. • When excluding exams with the lowest likelihood of cancer, the decrease in true-positive recalls would be balanced by a simultaneous reduction in false-positive recalls.
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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.