ArticleScientific reports2025
AI assistance improves reader agreement in digital mammography: A multireader crossover study of general and breast subspecialty radiologists.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- The Impact of Artificial Intelligence on BI-RADS Classification and Diagnostic Confidence in Mammography Interpretation by Radiology Residents.Life (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to evaluate the impact of artificial intelligence (AI) on inter- and intra-rater agreement in mammography interpretation, comparing improvements in reliability between general and breast subspecialty radiologists in a clinical setting. This study was conducted using anonymized digital mammograms from 65 women aged 40-74 years undergoing routine screening. Fourteen radiologists, grouped by experience, assessed images in a multi-reader, multi-case, crossover design with and without AI assistance. Statistical analyses, including Cohen's Kappa and meta-analysis, measured inter- and intra-rater reliability across radiological variables. AI assistance significantly improved agreement with the gold standard for both general and breast subspecialty radiologists. Variables such as BI-RADS breast density and lesion location showed marked improvements, particularly among general radiologists, where Kappa values for BI-RADS breast density rose from 50.01% to 81.38% with AI. Subspecialists demonstrated smaller performance gains, likely due to higher baseline accuracy. AI also enhanced intra-rater reliability and reduced variability across experience levels. These findings support AI's role as a valuable adjunct in breast cancer screening, addressing the shortage of experienced radiologists. Further research in real-world settings is necessary to confirm these results and optimize AI integration.
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.