ReviewRadiologic clinics of North America2021
Clinical Artificial Intelligence Applications: Breast Imaging.
Review in Radiologic clinics of North America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 41 citations in OpenAlex.
- Ten Years of Artificial Intelligence in Screening Mammography: A Systematic Review and Meta-Analysis of Diagnostic Accuracy and Clinical Implementation (Literature Published 2015-2025).Diagnostics (Basel, Switzerland) · 2026Review
- Review
- Artificial intelligence and breast cancer screening in Serbia: a dual-perspective qualitative study among radiologists and screening-aged women.Frontiers in radiology · 2026Article
- The Clinical Application of Artificial Intelligence in Breast Imaging: Current Insights, Challenges, and Future Directions.Journal of breast cancer · 2025Review
- Artificial Intelligence-Driven Personalization in Breast Cancer Screening: From Population Models to Individualized Protocols.Cancers · 2025Review
- Noninvasive Assessment of Vascular Endothelial Growth Factor and Prognosis in Gastric Cancer Through Radiomic Features.Clinical and translational gastroenterology · 2025Article
- Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review.Cureus · 2025Review
- Review
- A real world evaluation of an innovative artificial intelligence tool for population-level breast cancer screening.NPJ digital medicine · 2025Article
- Exploring AI Approaches for Breast Cancer Detection and Diagnosis: A Review Article.Breast cancer (Dove Medical Press) · 2025Review
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- p27 Cell Cycle Inhibitor and Survival in Luminal-Type Breast Cancer: Gene Ontology, Machine Learning, and Drug Screening Analysis.Journal of breast cancer · 2024Article
- AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- New Frontiers in Breast Cancer Imaging: The Rise of AI.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial Intelligence in Senology - Where Do We Stand and What Are the Future Horizons?European journal of breast health · 2024Article
- An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies.Current oncology (Toronto, Ont.) · 2024Review
- Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal-Ethical Considerations.The Eurasian journal of medicine · 2023Article
- Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review.Breast cancer research : BCR · 2023Review
- Artificial Intelligence in Breast X-Ray Imaging.Seminars in ultrasound, CT, and MR · 2023Review
- Updates in Artificial Intelligence for Breast Imaging.Seminars in roentgenology · 2022Review
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 at 2 institutions in 1 country.
Funding
Abstract
This article gives a brief overview of the development of artificial intelligence in clinical breast imaging. For multiple decades, artificial intelligence (AI) methods have been developed and translated for breast imaging tasks such as detection, diagnosis, and assessing response to therapy. As imaging modalities arise to support breast cancer screening programs and diagnostic examinations, including full-field digital mammography, breast tomosynthesis, ultrasound, and MRI, AI techniques parallel the efforts with more complex algorithms, faster computers, and larger data sets. AI methods include human-engineered radiomics algorithms and deep learning methods. Examples of these AI-supported clinical tasks are given along with commentary on the future.
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.