ArticleEuropean radiology2021
Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05048095 (The Use of AI as a Third Reader and During Consensus in a Double Reading Breast Cancer Screening Program in Sweden), which is not on this map. Cited by 50 papers, 3 of them syntheses that pooled it.
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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.
The Use of AI as a Third Reader and During Consensus in a Double Reading Breast Cancer Screening Program in Sweden
Who cites it
50 citing papers in PubMed, 3 syntheses or guidelines pooled it, 91 citations in OpenAlex.
- 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
- Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches.Biomedical engineering online · 2026Pooled it
- Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.Journal of medical Internet research · 2023Pooled it
- Noninferiority and Efficiency/Revenue Facilitation (NERF) Endpoints : Shifting Grounds of Argument in Health AI Interventional Studies.Journal of bioethical inquiry · 2026Article
- The Impact of Artificial Intelligence on BI-RADS Classification and Diagnostic Confidence in Mammography Interpretation by Radiology Residents.Life (Basel, Switzerland) · 2026Article
- Patient perceptions and attitudes towards the use of artificial intelligence in the symptomatic breast unit.European radiology · 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
- Diagnostic accuracy, fairness and clinical implementation of AI for breast cancer screening: results of multicenter retrospective and prospective technical feasibility studies.Nature cancer · 2026Article
- Sample size and power analysis for ROC AUC differences in diagnostic tests: a methodological evaluation of the Obuchowski-McClish and Hanley-McNeil methods.BMC medical research methodology · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- Development and evaluation of a deep incremental learning system for mammography image analysis.PloS one · 2026Article
- An ensemble machine learning model based on magnetic resonance imaging features for diagnosing deep infiltrating endometriosis.Frontiers in physiology · 2026Article
- Impact of artificial intelligence assistance on bone scintigraphy diagnosis.Physical and engineering sciences in medicine · 2025Article
- Workload reduction of digital breast tomosynthesis screening using artificial intelligence and synthetic mammography: a simulation study.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Mammographic classification of interval breast cancers and artificial intelligence performance.Journal of the National Cancer Institute · 2025Article
- An Exploration of Discrepant Recalls Between AI and Human Readers of Malignant Lesions in Digital Mammography Screening.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review.EClinicalMedicine · 2025Article
- A preliminary investigation into the potential, pitfalls, and limitations of large language models for mammography interpretation.Discover oncology · 2025Article
- A real world evaluation of an innovative artificial intelligence tool for population-level breast cancer screening.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesDigital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist.
methodsA multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance.
resultsOn average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39-42 s) to 36 s (95% CI = 35- 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115).
conclusionsRadiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system. KEY POINTS: • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.
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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.