ReviewJournal of breast imaging2020
Artificial Intelligence: A Primer for Breast Imaging Radiologists.
Review in Journal of breast imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption.Journal of medical Internet research · 2025Pooled it
- Advances in Breast Cancer Diagnostics: From Screening to Precision Medicine.Diagnostics (Basel, Switzerland) · 2026Review
- Performance of an Artificial Intelligence Support System on Screening Mammography Cases Proceeding to Stereotactic Biopsy.Cancers · 2025Article
- AI assistance improves reader agreement in digital mammography: A multireader crossover study of general and breast subspecialty radiologists.Scientific reports · 2025Article
- Impact of AI on Breast Cancer Detection Rates in Mammography by Radiologists of Varying Experience Levels in Singapore: Preliminary Comparative Study.JMIR formative research · 2025Article
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- Acceptability of artificial intelligence in breast screening: focus groups with the screening-eligible population in England.BMJ public health · 2024Article
- AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway.European radiology · 2024Article
- Combining AI and Radiomics to Improve the Accuracy of Breast US.Radiology · 2024Article
- Artificial Intelligence (AI)-Based Computer-Assisted Detection and Diagnosis for Mammography: An Evidence-Based Review of Food and Drug Administration (FDA)-Cleared Tools for Screening Digital Breast Tomosynthesis (DBT).AI in precision oncology · 2024Review
- Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review.European radiology · 2024Article
- Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal-Ethical Considerations.The Eurasian journal of medicine · 2023Article
- Consistency of Artificial Intelligence (AI)-based Diagnostic Support Software in Short-term Digital Mammography Reimaging After Core Needle Biopsy.Journal of digital imaging · 2023Article
- Artificial Intelligence Applied to Contrast-enhanced Mammography: Exploring Uncharted Territory.Radiology · 2023Article
- Artificial Intelligence in Breast X-Ray Imaging.Seminars in ultrasound, CT, and MR · 2023Review
- Prediction of axillary lymph node metastasis in early breast cancer patients with ultrasonic videos based deep learning.Frontiers in oncology · 2023Article
- Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review.Diagnostics (Basel, Switzerland) · 2022Review
- External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence.JAMA network open · 2022Article
- Updates in Artificial Intelligence for Breast Imaging.Seminars in roentgenology · 2022Review
- Research Highlight: Artificial Intelligence for Ruling Out Negative Examinations in Screening Breast MRI.Korean journal of radiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimately ensure optimal patient care.
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.