ArticleAJR. American journal of roentgenology2019
Global Trend in Artificial Intelligence-Based Publications in Radiology From 2000 to 2018.
Article 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. Cited by 27 papers, 3 of them syntheses 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
27 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review.Journal of digital imaging · 2023Pooled it
- Pooled it
- Predicting BRAFV600E mutations in papillary thyroid carcinoma using six machine learning algorithms based on ultrasound elastography.Scientific reports · 2023Trial
- Artificial Intelligence in Endodontics-A Bibliometric Analysis.Australian endodontic journal : the journal of the Australian Society of Endodontology Inc · 2026Review
- State and Diffusion of National Institutes of Health Funding of AI in Radiology.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities.Emergency radiology · 2025Article
- Global output of clinical application research on artificial intelligence in the past decade: a scientometric study and science mapping.Systematic reviews · 2025Article
- Diagnostic Performance of an Artificial Intelligence Software for the Evaluation of Bone X-Ray Examinations Referred from the Emergency Department.Diagnostics (Basel, Switzerland) · 2025Article
- Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.Investigative radiology · 2025Review
- Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review.PLOS digital health · 2024Article
- An ASER AI/ML expert panel formative user research study for an interpretable interactive splenic AAST grading graphical user interface prototype.Emergency radiology · 2024Article
- Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel.Emergency radiology · 2023Article
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- The current and future roles of artificial intelligence in pediatric radiology.Pediatric radiology · 2022Review
- Mitigating Bias in Radiology Machine Learning: 1. Data Handling.Radiology. Artificial intelligence · 2022Article
- Code and Data Sharing Practices in the Radiology Artificial Intelligence Literature: A Meta-Research Study.Radiology. Artificial intelligence · 2022Article
- Analysis of international publication trends in artificial intelligence in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Review
- LASSO-based machine learning models for the prediction of central lymph node metastasis in clinically negative patients with papillary thyroid carcinoma.Frontiers in endocrinology · 2022Article
- A comparative analysis of eight machine learning models for the prediction of lateral lymph node metastasis in patients with papillary thyroid carcinoma.Frontiers in endocrinology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
31414886What 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.