ArticleJournal of medical radiation sciences2026
Radiation Risk in 2D Mammography Screening: A Scoping Review of Modelling Strategies and Emerging AI Applications.
Article in Journal of medical radiation sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Radiation dose comparison of photon-counting breast CT and mammography: A phantom study.European journal of radiology open · 2026Article
- Fluorescence properties of methylene blue conjugated to normal/cancerous human tissues for spectral discrimination of breast cancer stages (I-IV).Biomedical optics express · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most commonly diagnosed cancer among women worldwide, and concerns regarding radiation exposure from mammography screening remain a potential barrier to participation. This scoping review explores existing models estimating long-term radiation risks associated with repeated mammography screening. A structured search across five databases (Medline, Embase, Scopus, Web of Science and CINAHL) along with manual searching identified 24 studies published between 2014 and 2024. These were categorised into three themes: (1) models estimating dose-risk profiles, (2) factors affecting radiation dose and (3) the use of artificial intelligence (AI) in dose estimation and mammographic breast density (MBD) estimation. Studies showed that breast density, compressed breast thickness (CBT) and technical imaging parameters significantly influence mean glandular dose (MGD). Modelling studies highlighted the low risk of radiation-induced cancer, inconsistencies in protocols and vendor-specific limitations. AI applications are emerging as promising tools for improving individualised dose-risk assessments but require further development for compatibility across different imaging platforms.
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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.