ArticleEuropean radiology2021
Can artificial intelligence reduce the interval cancer rate in mammography screening?
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, 2 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.
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, 2 syntheses or guidelines pooled it, 95 citations in OpenAlex.
- Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review.Journal of the American College of Radiology : JACR · 2024Pooled 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
- 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
- From algorithms to patient outcomes - lessons from one of the first randomized trials of AI in medicine.Nature medicine · 2026Article
- A comparison of tumour characteristics of screen-detected and interval cancers and their relationship with breast density and AJCC prognostic stage in a UK screening cohort.European radiology · 2026Article
- Can AI models detect abnormality in screening mammography years before confirmed diagnosis?European radiology · 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
- Missed and true interval cancers on digital breast tomosynthesis screening mammograms: radiologists' assessment and artificial intelligence markings.Acta radiologica open · 2026Article
- Location of AI risk markers and associated mammographic features in screening mammograms obtained years before screen-detected breast cancer.European radiology · 2026Article
- Enhancing Early Detection of Contralateral Breast Cancer in Breast Cancer Survivors Using AI-Assisted Mammography.Annals of surgical oncology · 2026Article
- Viewpoint on the Consequences and Mitigation of Cognitive Bias in the Radiological Interpretation of Breast Cancer Imaging Using Artificial Intelligence.JMIR medical informatics · 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
- Performance of breast cancer risk prediction algorithms across mammography systems in the UK screening programme.NPJ digital medicine · 2026Article
- Retrospective evaluation of interval breast cancer screening mammograms by radiologists and AI.European radiology · 2026Article
- Results from a Swedish model-based analysis of the cost-effectiveness of AI-assisted digital mammography.European radiology · 2026Article
- The evaluation of artificial intelligence in mammography-based breast cancer screening: Is breast-level analysis enough?European radiology · 2025Article
- Improving Benign and Malignant Classifications in Mammography with ROI-Stratified Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Mammographic classification of interval breast cancers and artificial intelligence performance.Journal of the National Cancer Institute · 2025Article
- Cancers not detected in one-view breast tomosynthesis screening-characteristics and reasons for non-detection.European radiology · 2025Article
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Corrections and comments
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Authors and funding
4 authors at 2 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening. MATERIALS AND
methodsPreceding screening mammograms of 429 consecutive women diagnosed with interval cancer in Southern Sweden between 2013 and 2017 were analysed with a deep learning-based AI system. The system assigns a risk score from 1 to 10. Two experienced breast radiologists reviewed and classified the cases in consensus as true negative, minimal signs or false negative and assessed whether the AI system correctly localised the cancer. The potential reduction of interval cancer was calculated at different risk score thresholds corresponding to approximately 10%, 4% and 1% recall rates.
resultsA statistically significant correlation between interval cancer classification groups and AI risk score was observed (p < .0001). AI scored one in three (143/429) interval cancer with risk score 10, of which 67% (96/143) were either classified as minimal signs or false negative. Of these, 58% (83/143) were correctly located by AI, and could therefore potentially be detected at screening with the aid of AI, resulting in a 19.3% (95% CI 15.9-23.4) reduction of interval cancer. At 4% and 1% recall thresholds, the reduction of interval cancer was 11.2% (95% CI 8.5-14.5) and 4.7% (95% CI 3.0-7.1). The corresponding reduction of interval cancer with grave outcome (women who died or with stage IV disease) at risk score 10 was 23% (8/35; 95% CI 12-39).
conclusionThe use of AI in screen reading has the potential to reduce the rate of interval cancer without supplementary screening modalities. KEY POINTS: • Retrospective study showed that AI detected 19% of interval cancer at the preceding screening exam that in addition showed at least minimal signs of malignancy. Importantly, these were correctly localised by AI, thus obviating supplementary screening modalities. • AI could potentially reduce a proportion of particularly aggressive interval cancers. • There was a correlation between AI risk score and interval cancer classified as true negative, minimal signs or false negative.
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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.