ReviewJournal of the Korean Society of Radiology2026
AI for Breast Cancer Risk Prediction Beyond Breast Density.
Review in Journal of the Korean Society of Radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
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0 citing papers in PubMed.
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
AI has been increasingly adopted for breast cancer screening to improve cancer detection. Its role in mammography has expanded from cancer detection to image-based risk prediction. Recent advances have enabled AI to integrate subtle early imaging signs of breast cancer and mammographic patterns beyond breast density. Since the introduction of Mirai, an AI model designed to predict breast cancer risk within five years, research on mammography-based risk prediction has been growing. Multiple studies have demonstrated that AI models achieve superior performance compared to traditional clinical risk models across diverse screening populations. This advantage is reported to be greater in advanced breast cancers. An accurate risk assessment enables the identification of women who may benefit from supplemental breast cancer screening. This review provides an overview of the applications of AI in mammography in the era of breast cancer risk prediction, its medical evidence to date, and suggestions for future directions.
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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.