ArticleAJNR. American journal of neuroradiology2020
Artificial Intelligence in Neuroradiology: Current Status and Future Directions.
Article in AJNR. American journal of neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies.Frontiers in oncology · 2022Pooled it
- Artificial intelligence in acute hydrocephalus: Narrowing the diagnostic gap.European radiology · 2026Article
- Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography.Scientific reports · 2026Article
- Artificial intelligence in contact sports research: Promise and peril for predicting and preventing brain injury.Experimental physiology · 2026Article
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- Neurosurgery 4.0: A Scoping Review of Current Trends and Challenges in Specialized Smart Hospitals.Cureus · 2025Review
- Detection of antibodies in suspected autoimmune encephalitis diseases using machine learning.Scientific reports · 2025Article
- Evaluation of Siemens Healthineers' StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke.Frontiers in neurology · 2025Article
- Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology.AJNR. American journal of neuroradiology · 2024Article
- Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.AJNR. American journal of neuroradiology · 2024Article
- Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.Scientific reports · 2024Article
- Article
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- The Role of Artificial Intelligence-Powered Imaging in Cerebrovascular Accident Detection.Cureus · 2024Review
- Article
- Transforming medicine: artificial intelligence integration in the peripheral nervous system.Frontiers in neurology · 2024Review
- A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities.Frontiers in artificial intelligence · 2024Review
- Revolutionizing pediatric neuroimaging: the era of CT, MRI, and beyond.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2023Review
- Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Critical Appraisal of Artificial Intelligence-Enabled Imaging Tools Using the Levels of Evidence System.AJNR. American journal of neuroradiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.
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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.