ReviewJournal of magnetic resonance imaging : JMRI2024
AI Applications to Breast MRI: Today and Tomorrow.
Review in Journal of magnetic resonance imaging : JMRI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 14 citations in OpenAlex.
- Can AI safely triage abbreviated breast MRI? Bridging efficiency and diagnostic confidence.European radiology · 2026Article
- A Practical Machine Learning Model for Predicting Neoadjuvant Response in HER2-Positive Breast Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence for Breast MRI Lesion Classification: A Targeted Evidence Synthesis and Meta-Analysis of Discriminative Performance and Heterogeneity.Diagnostics (Basel, Switzerland) · 2026Review
- Deep Learning for Early Breast Cancer Detection on Contrast-Enhanced Breast MRI: A Multicenter Study.Cancers · 2026Article
- Establishing an Evidence-based Modern Breast MRI Program.Journal of breast imaging · 2026Review
- An Automated Hybrid Deep Learning-based Model for Breast Cancer Detection using Mammographic ImagesCurrent medical imaging · 2026Article
- AI-driven multi-omics integration in breast cancer: clinical applications, immunotherapy prediction, and translational challenges.Frontiers in molecular biosciences · 2026Review
- Including AI in diffusion-weighted breast MRI has potential to increase reader confidence and reduce workload.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Exploring personalized neoadjuvant therapy selection strategies in breast cancer: an explainable multi-modal response model.EClinicalMedicine · 2025Article
- Exploring MRI and Mammography Lesion Features for Breast Cancer Detection in PTEN Hamartoma Tumor Syndrome.Cancers · 2025Article
- Assessment of mammographic and ultrasonic signatures for differentiating benign and malignant breast structural distortions.American journal of translational research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
Indexed as
Identifiers
What Socratic 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 Socratic graph.