ArticleRadiology. Artificial intelligence2024
AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study.
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20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial-intelligence models vs. radiologists in the detection of clinically significant prostate cancer on mpMRI: a meta-analysis.European radiology · 2026Pooled it
- 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
- 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 health systems of the WHO European region: implementation, applications, opportunities, and barriers.International journal of medical informatics · 2026Article
- AI performance varies considerably across mammography devices: a multi-site and multi-vendor retrospective study.Insights into imaging · 2026Article
- Impact of AI assistance on reading time, cancer detection rate, and abnormal interpretation rate in screening and diagnostic mammography: a prospective alternating-month study.European radiology · 2026Article
- Artificial intelligence in breast cancer screening: A systematic review and meta-analysis of integration strategies.European journal of radiology open · 2026Review
- Reducing false positives in breast cancer screening with computer-aided detection.Clinical and experimental medicine · 2026Article
- Acceptability of Using Artificial Intelligence in the National Health Service Breast Screening Program: A Randomized Online Survey of Screening-Eligible Women in England.Mayo Clinic proceedings. Digital health · 2026Article
- Impact of using artificial intelligence as a second reader in breast screening including arbitration.Nature cancer · 2026Article
- External evaluation of an open-source deep learning model for prostate cancer detection on bi-parametric MRI.European radiology · 2026Article
- Retrospective evaluation of interval breast cancer screening mammograms by radiologists and AI.European radiology · 2026Article
- Human-AI interaction in a cancer-enriched double-reading breast screening cohort: diagnostic accuracy and second-reader behavior.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Emerging Approaches in Breast Cancer: From Molecular Mechanisms to Diagnosis and Therapeutic Strategies.Oncology research · 2026Review
- Transparency of medical artificial intelligence systems.Nature reviews bioengineering · 2026Article
- Over-detection and over-surveillance in breast screening: current status and the potential for artificial intelligence optimisation.Insights into imaging · 2025Review
- Improving Benign and Malignant Classifications in Mammography with ROI-Stratified Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy.Life (Basel, Switzerland) · 2024Article
- AI as a Second Reader Can Reduce Radiologists' Workload and Increase Accuracy in Screening Mammography.Radiology. Artificial intelligence · 2024Article
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7 authors.
Mohammad T ElhakimFrom the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.).ORCID 0000-0002-2673-2352
Sarah W StougaardFrom the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.).ORCID 0000-0002-2654-5469
Ole GraumannFrom the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.).ORCID 0000-0002-9663-8361
Mads NielsenFrom the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.).ORCID 0000-0003-1535-068X
Oke GerkeFrom the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.).ORCID 0000-0001-6335-3303
Lisbet B LarsenFrom the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.).
Benjamin S B RasmussenFrom the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.).ORCID 0000-0002-7357-0755
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mammography screening supported by deep learning-based artificial intelligence (AI) solutions can potentially reduce workload without compromising breast cancer detection accuracy, but the site of deployment in the workflow might be crucial. This retrospective study compared three simulated AI-integrated screening scenarios with standard double reading with arbitration in a sample of 249 402 mammograms from a representative screening population. A commercial AI system replaced the first reader (scenario 1: integrated AI
Indexed as
Breast NeoplasmsFeasibility StudiesMammographyAgedArtificial IntelligenceDeep LearningEarly Detection of CancerFemaleHumansMass ScreeningMiddle AgedRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityBreastConvolutional Neural Network (CNN)DiagnosisEpidemiologyMammographyNeoplasms-PrimaryScreening
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