ReviewJournal of breast cancer2025
The Clinical Application of Artificial Intelligence in Breast Imaging: Current Insights, Challenges, and Future Directions.
Review in Journal of breast cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Machine Learning-Based Classification of BI-RADS 4 and BI-RADS 5 Microcalcifications in Mammography Combined with DCE-MRI for Malignant-Benign Discrimination.Tomography (Ann Arbor, Mich.) · 2026Article
- Breast Ultrasound AI Under Dataset Shift: A Patient-Leakage-Aware Benchmark.Diagnostics (Basel, Switzerland) · 2026Article
- Effect of a Real-Time Artificial Intelligence-Assisted Ultrasound System on BI-RADS C4 Breast Lesions Based on Breast Density.Cancers · 2026Article
- AI-mediated ultrasound radiomics in the diagnosis and treatment of triple-negative breast cancer: research progress and future challenges.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is used in various areas of radiology, particularly in breast imaging, starting with mammography and extending to ultrasonography (US) and magnetic resonance imaging (MRI). This overview aims to examine the introduction, applications, and challenges of AI in breast imaging. This narrative outlines the applications of AI in various modalities-including mammography, US, and MRI-and discusses its indications, ongoing challenges, and future perspectives. AI has been used for identification, classification, detection, diagnosis, breast density assessment, treatment response, and prediction of prognosis. AI can help radiologists avoid missed diagnoses due to heavy workloads and enhance workflow efficiency. The integration of AI software into daily practice, along with further validation and refinement, is necessary to support radiologists' workflows.
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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.