ArticleRadiology. Artificial intelligence2023
Impact of Different Mammography Systems on Artificial Intelligence Performance in Breast Cancer Screening.
Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- 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
- Current status and future directions of AI in prostate cancer detection on MRI: a special report from the ESUR prostate MRI working group authors.European radiology · 2026Article
- AI performance varies considerably across mammography devices: a multi-site and multi-vendor retrospective study.Insights into imaging · 2026Article
- Subcategorisation of Data for AI Models in Healthcare: A Case Study in Mammography.Diagnostics (Basel, Switzerland) · 2026Article
- X-Pruning: a dual-stream information fusion mammography diagnosis network based on pruned transformer and cross-attention mechanism.Quantitative imaging in medicine and surgery · 2026Article
- A National Audit of Mammography Systems Settings That May Affect the Output of Artificial Intelligence Software.Diagnostics (Basel, Switzerland) · 2026Article
- Explainable Artificial Intelligence in Mammography: A Systematic Review of Methods, Evaluation Practices, and Clinical Readiness.Diagnostics (Basel, Switzerland) · 2026Review
- 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
- Prospective evaluation of artificial intelligence integration into breast cancer screening in multiple workflow settings: the GEMINI study.Nature cancer · 2026Article
- Impact of using artificial intelligence as a second reader in breast screening including arbitration.Nature cancer · 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
- Establishing a framework for development, prioritization, and assessment of artificial intelligence technology within the radiology department at a large UK teaching hospital.BJR artificial intelligence · 2026Review
- Identifying and understanding significant change due to drift when assessing AI models in healthcare: a narrative review.BMJ digital health & AI · 2026Review
- Assessing the generalisation of artificial intelligence across mammography manufacturers.PLOS digital health · 2025Article
- Article
- Challenges for implementing generative artificial intelligence (GenAI) into clinical healthcare.Internal medicine journal · 2025Review
- Inconsistency of AI in intracranial aneurysm detection with varying dose and image reconstruction.Scientific reports · 2025Article
- Assessing artificial intelligence in breast screening with stratified results on 306 839 mammograms across geographic regions, age, breast density and ethnicity: A Retrospective Investigation Evaluating Screening (ARIES) study.BMJ health & care informatics · 2025Article
- Article
- Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow EfficiencyCurrent medical imaging · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) tools may assist breast screening mammography programs, but limited evidence supports their generalizability to new settings. This retrospective study used a 3-year dataset (April 1, 2016-March 31, 2019) from a U.K. regional screening program. The performance of a commercially available breast screening AI algorithm was assessed with a prespecified and site-specific decision threshold to evaluate whether its performance was transferable to a new clinical site. The dataset consisted of women (aged approximately 50-70 years) who attended routine screening, excluding self-referrals, those with complex physical requirements, those who had undergone a previous mastectomy, and those who underwent screening that had technical recalls or did not have the four standard image views. In total, 55 916 screening attendees (mean age, 60 years ± 6 [SD]) met the inclusion criteria. The prespecified threshold resulted in high recall rates (48.3%, 21 929 of 45 444), which reduced to 13.0% (5896 of 45 444) following threshold calibration, closer to the observed service level (5.0%, 2774 of 55 916). Recall rates also increased approximately threefold following a software upgrade on the mammography equipment, requiring per-software version thresholds. Using software-specific thresholds, the AI algorithm would have recalled 277 of 303 (91.4%) screen-detected cancers and 47 of 138 (34.1%) interval cancers. AI performance and thresholds should be validated for new clinical settings before deployment, while quality assurance systems should monitor AI performance for consistency.
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