ReviewTomography (Ann Arbor, Mich.)2024
A Review of Artificial Intelligence in Breast Imaging.
Review in Tomography (Ann Arbor, Mich.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07558746 (AI Reliance in Diagnostic Radiology Among Intern Doctors in Palestine), which is not on this map. Cited by 19 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.
AI Reliance in Diagnostic Radiology Among Intern Doctors in Palestine: A Triple-Arm, Triple-Blind, Parallel-Design Randomized Controlled Trial
Who cites it
19 citing papers in PubMed.
- Review
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- ITCNET: a visual and language pretraining model based on multimodal fusion in predicting the breast cancer subtypes on DCE-MRI.BMC medical informatics and decision making · 2026Article
- CMT-BUSNet: Adaptive Fusion-Based Triple-Branch Hybrid Architecture for Explainable Breast Ultrasound Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence and breast cancer screening in Serbia: a dual-perspective qualitative study among radiologists and screening-aged women.Frontiers in radiology · 2026Article
- Benchmarking large language models against clinicians across hospital levels in cardiovascular decision-making: a cross-sectional vignette-based study.Scientific reports · 2025Article
- Explainable Deep Learning for Breast Lesion Classification in Digital and Contrast-Enhanced Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- Review
- Optimized YOLOv8 for enhanced breast tumor segmentation in ultrasound imaging.Discover oncology · 2025Article
- AGO Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2025.Breast care (Basel, Switzerland) · 2025Review
- Artificial Intelligence as a Potential Tool for Predicting Surgical Margin Status in Early Breast Cancer Using Mammographic Specimen Images.Diagnostics (Basel, Switzerland) · 2025Article
- AI in 2D Mammography: Improving Breast Cancer Screening Accuracy.Medicina (Kaunas, Lithuania) · 2025Article
- Evolution of an Artificial Intelligence-Powered Application for Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Assessment of mammographic and ultrasonic signatures for differentiating benign and malignant breast structural distortions.American journal of translational research · 2025Article
- Article
- Artificial intelligence versus radiologists in detecting early-stage breast cancer from mammograms: a meta-analysis of paradigm shifts.Polish journal of radiology · 2025Review
- MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution.Frontiers in oncology · 2025Article
- Artificial intelligence-based automated breast ultrasound radiomics for breast tumor diagnosis and treatment: a narrative review.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.
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.