ArticleBMC cancer2023
Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms.
Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in mammography: a systematic review of the external validation.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2024Pooled it
- Noninferiority and Efficiency/Revenue Facilitation (NERF) Endpoints : Shifting Grounds of Argument in Health AI Interventional Studies.Journal of bioethical inquiry · 2026Article
- AI performance varies considerably across mammography devices: a multi-site and multi-vendor retrospective study.Insights into imaging · 2026Article
- Artificial intelligence in breast cancer screening: A systematic review and meta-analysis of integration strategies.European journal of radiology open · 2026Review
- Prospective evaluation of artificial intelligence integration into breast cancer screening in multiple workflow settings: the GEMINI study.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
- Impact of using artificial intelligence as a second reader in breast screening including arbitration.Nature cancer · 2026Article
- The evaluation of artificial intelligence in mammography-based breast cancer screening: Is breast-level analysis enough?European radiology · 2025Article
- Medical machine learning operations: a framework to facilitate clinical AI development and deployment in radiology.European radiology · 2025Review
- Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery.Current oncology (Toronto, Ont.) · 2025Review
- 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
- The Future of Breast Cancer Organized Screening Program Through Artificial Intelligence: A Scoping Review.Healthcare (Basel, Switzerland) · 2025Review
- AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study.Radiology. Artificial intelligence · 2024Article
- AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway.European radiology · 2024Article
- Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer.Nature communications · 2024Article
- A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 2: A technical introduction.Journal of experimental orthopaedics · 2024Review
- Review
- The promise of AI in personalized breast cancer screening: are we there yet?Nature reviews. Clinical oncology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
backgroundDouble reading (DR) in screening mammography increases cancer detection and lowers recall rates, but has sustainability challenges due to workforce shortages. Artificial intelligence (AI) as an independent reader (IR) in DR may provide a cost-effective solution with the potential to improve screening performance. Evidence for AI to generalise across different patient populations, screening programmes and equipment vendors, however, is still lacking.
methodsThis retrospective study simulated DR with AI as an IR, using data representative of real-world deployments (275,900 cases, 177,882 participants) from four mammography equipment vendors, seven screening sites, and two countries. Non-inferiority and superiority were assessed for relevant screening metrics.
resultsDR with AI, compared with human DR, showed at least non-inferior recall rate, cancer detection rate, sensitivity, specificity and positive predictive value (PPV) for each mammography vendor and site, and superior recall rate, specificity, and PPV for some. The simulation indicates that using AI would have increased arbitration rate (3.3% to 12.3%), but could have reduced human workload by 30.0% to 44.8%.
conclusionsAI has potential as an IR in the DR workflow across different screening programmes, mammography equipment and geographies, substantially reducing human reader workload while maintaining or improving standard of care.
trial registrationISRCTN18056078 (20/03/2019; retrospectively registered).
Indexed as
Identifiers
What OpenQuestion holds
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.