ArticleNature communications2025
Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 40 papers, 3 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.
Artificial Intelligence for breaST canceR scrEening in mAMmography (AI-STREAM): A Prospective, Multicenter Cohort Study
Use of Artificial Intelligence in Breast Cancer Screening: Impact of AI-Assisted Single Reading by One Radiologist on Screening Quality Indicators Compared to Standard Double Reading by Two Radiologists Without AI
Who cites it
40 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.La Radiologia medica · 2026Pooled it
- Performance of artificial intelligence in breast cancer screening programmes: a systematic review.BMJ open · 2025Pooled it
- Systematic Review and Meta-Analysis of AI-Assisted Mammography and the Systemic Immune-Inflammation Index in Breast Cancer: Diagnostic and Prognostic Perspectives.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Merging artificial intelligence into cancer nursing care: Current applications, challenges, and opportunities.Asia-Pacific journal of oncology nursing · 2026Article
- Verification of the impact of differences between objective and subjective evaluation methods on the interpretation of artificial intelligence systems generating mammogram reports using vision-language model.Japanese journal of radiology · 2026Article
- Large-scale esophageal cancer screening through noncontrast computed tomography and artificial intelligence.Nature medicine · 2026Article
- 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
- Implementation of an artificial intelligence-based system for mammography in the compulsory medical insurance program: results of a 3-year study.Quantitative imaging in medicine and surgery · 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
- Exploring shear-wave elastography and apparent diffusion coefficient in breast lesion characterization: diagnostic accuracy and inter-modality correlation.La Radiologia medica · 2026Article
- AI-assisted digital breast tomosynthesis as a decision-support tool for post-neoadjuvant surgical planning in breast cancer.Langenbeck's archives of surgery · 2026Article
- Hybrid metaheuristic feature selection for breast cancer detection in digital mammography: a radiomics and deep learning pilot feasibility study.BMC medical imaging · 2026Article
- Barriers, Facilitators, and Intention to Use AI for Breast Cancer Diagnosis: Mixed Methods Study Among Austrian Physicians With and Without AI Experience.Journal of medical Internet research · 2026Article
- Reading the future: how AI rewrites mammography workflows.European radiology · 2026Article
- Current and future perspectives of molecular breast imaging with [European journal of nuclear medicine and molecular imaging · 2026Review
- Performance across different versions of an artificial intelligence model for screen-reading of mammograms.European radiology · 2026Article
- Artificial Intelligence for Mammography Ready for the Real-World: Turning Promise Into Reality.Korean journal of radiology · 2026Article
- Explainable Artificial Intelligence in Mammography: A Systematic Review of Methods, Evaluation Practices, and Clinical Readiness.Diagnostics (Basel, Switzerland) · 2026Review
- Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) improves the accuracy of mammography screening, but prospective evidence, particularly in a single-read setting, remains limited. This study compares the diagnostic accuracy of breast radiologists with and without AI-based computer-aided detection (AI-CAD) for screening mammograms in a real-world, single-read setting. A prospective multicenter cohort study is conducted within South Korea's national breast cancer screening program for women. The primary outcomes are screen-detected breast cancer within one year, with a focus on cancer detection rates (CDRs) and recall rates (RRs) of radiologists. A total of 24,543 women are included in the final cohort, with 140 (0.57%) screen-detected breast cancers. The CDR is significantly higher by 13.8% for breast radiologists using AI-CAD (n = 140 [5.70‰]) compared to those without AI (n = 123 [5.01‰]; p < 0.001), with no significant difference in RRs (p = 0.564). These preliminary results show a significant improvement in CDRs without affecting RRs in a radiologist's standard single-reading setting (ClinicalTrials.gov: NCT05024591).
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.