ArticleRadiology. Artificial intelligence2021
Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage.
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it, 89 citations in OpenAlex.
- Systematic review on the impact of deep learning-driven worklist triage on radiology workflow and clinical outcomes.European radiology · 2025Pooled it
- Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- Real-world performance and misclassification patterns of deep learning for intracranial hemorrhage detection.Neuroradiology · 2026Article
- Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography.Scientific reports · 2026Article
- Learning neuroimaging models from health system-scale data.Nature biomedical engineering · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes.NPJ digital medicine · 2026Article
- Performance Evaluation of a Commercial Deep Learning Software for Detecting Intracranial Hemorrhage in a Pediatric Population.Journal of imaging informatics in medicine · 2026Article
- Unveiling the Benefits of Artificial Intelligence in Individual, Organizational Management, and the Health/Sector System.Health science reports · 2026Review
- Automated AI detection of thoracic aortic dissection on CT imaging.European radiology experimental · 2025Article
- Emergency radiology: roadmap for radiology departments.Japanese journal of radiology · 2025Review
- Accuracy of Large Language Models in Detecting Cases Requiring Immediate Reporting in Pediatric Radiology: A Feasibility Study Using Publicly Available Clinical Vignettes.Korean journal of radiology · 2025Article
- Real-Life Performance of a Commercially Available AI Tool for Post-Traumatic Intracranial Hemorrhage Detection on CT Scans: A Supportive Tool.Journal of clinical medicine · 2025Article
- Improving Turnaround Times and Operational Efficiency in Radiology Services: Quality Improvement Study in Oman.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Learning neuroimaging models from health system-scale data.Research square · 2025Article
- The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection.Frontiers in medicine · 2025Review
- Artificial Intelligence for Detecting Pulmonary EmbolismsCurrent medical imaging · 2025Article
- Diagnostic performance and clinical applications of artificial intelligence for intracranial bleeding detection: A meta-analysis.Brain & spine · 2025Article
- Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans.Life (Basel, Switzerland) · 2024Article
- Effects of artificial intelligence implementation on efficiency in medical imaging-a systematic literature review and meta-analysis.NPJ digital medicine · 2024Review
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo determine how to optimize the delivery of machine learning techniques in a clinical setting to detect intracranial hemorrhage (ICH) on non-contrast-enhanced CT images to radiologists to improve workflow. MATERIALS AND
methodsIn this study, a commercially available machine learning algorithm that flags abnormal noncontrast CT examinations for ICH was implemented in a busy academic neuroradiology practice between September 2017 and March 2019. The algorithm was introduced in three phases:
resultsNotification with a widget or flagging the examination had no effect on queue-adjusted image wait (
conclusionThe approach used to present flags from artificial intelligence and machine learning algorithms to the radiologist can reduce image wait time and turnaround times.© RSNA, 2021See also the commentary by O'Connor and Bhalla in this issue.
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.