ReviewAJR. American journal of roentgenology2019
New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence.
Review in AJR. American journal of roentgenology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 44 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
44 citing papers in PubMed, 119 citations in OpenAlex.
- Safety design guidelines for clinician-AI interaction in computer-aided diagnosis systems using system-theoretic framework with explainability validation.Scientific reports · 2026Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Hybrid transformer-based model for mammogram classification by integrating prior and current images.Medical physics · 2025Article
- Artificial Intelligence and Breast Cancer Management: From Data to the Clinic.Cancer innovation · 2025Review
- Controversies in the Application of AI in Radiology-Is There Medico-Legal Support? Aspects from Romanian Practice.Diagnostics (Basel, Switzerland) · 2025Review
- Review
- The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.Insights into imaging · 2024Article
- Synergistic use of handcrafted and deep learning features for tomato leaf disease classification.Scientific reports · 2024Article
- CSA-Net: Channel and Spatial Attention-Based Network for Mammogram and Ultrasound Image Classification.Journal of imaging · 2024Article
- Colorectal cancer histopathology image analysis: A comparative study of prognostic values of automatically extracted morphometric nuclear features in multispectral and red-blue-green imagery.Histology and histopathology · 2024Article
- Downgrading Breast Imaging Reporting and Data System categories in ultrasound using strain elastography and computer-aided diagnosis system: a multicenter, prospective study.The British journal of 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
- Artificial intelligence-based classification of breast nodules: a quantitative morphological analysis of ultrasound images.Quantitative imaging in medicine and surgery · 2024Article
- Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography.Korean journal of radiology · 2024Article
- Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review.European radiology · 2024Article
- Artificial Intelligence-What to Expect From Machine Learning and Deep Learning in Hernia Surgery.Journal of abdominal wall surgery : JAWS · 2024Review
- Gray-to-color image conversion in the classification of breast lesions on ultrasound using pre-trained deep neural networks.Medical & biological engineering & computing · 2023Article
- Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model.Heliyon · 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
- A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
- Erratum issuedCorrections.2019
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThe purpose of this article is to compare traditional versus machine learning-based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, to underscore limitations of traditional CAD, and to highlight potential solutions in new CAD systems under development for the future.
conclusionCAD development for breast imaging is undergoing a paradigm shift based on vast improvement of computing power and rapid emergence of advanced deep learning algorithms, heralding new systems that may hold real potential to improve clinical 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.