ReviewInformatics in medicine unlocked2021
The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis.
Review in Informatics in medicine unlocked, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Comparative accuracy of artificial intelligence versus manual interpretation in detecting pulmonary hypertension across chest imaging modalities: a diagnostic test accuracy meta-analysis.Frontiers in artificial intelligence · 2025Pooled it
- The accuracy of machine learning approaches using non-image data for the prediction of COVID-19: A meta-analysis.International journal of medical informatics · 2022Pooled it
- Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis.Academic radiology · 2021Pooled it
- Enhanced Glioblastoma Detection ThroughMagnetic resonance in medicine · 2026Article
- Artificial intelligence-powered chest computed tomography analysis unveils prognostic insights for COVID-19 mortality among prevalent hemodialysis patients.Kidney research and clinical practice · 2026Article
- Towards Accountable AI in Eye Disease Diagnosis: Workflow, External Validation, and Development.ArXiv · 2025Article
- AI Workflow, External Validation, and Development in Eye Disease Diagnosis.JAMA network open · 2025Article
- Automated detection of Alzheimer's disease: a multi-modal approach with 3D MRI and amyloid PET.Scientific reports · 2024Article
- Coronavirus Anatomy and Its Analytical Approaches for Targeting COVID-19.Advances in experimental medicine and biology · 2024Review
- Detection of Alzheimer's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning.Neural regeneration research · 2023Review
- Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.Cureus · 2023Review
- Monitoring of pulmonary involvement in critically ill COVID-19 patients - should lung ultrasound be preferred over CT?The ultrasound journal · 2023Article
- Critical roles of cytokine storm and bacterial infection in patients with COVID-19: therapeutic potential of mesenchymal stem cells.Inflammopharmacology · 2023Review
- Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation.Biomedical signal processing and control · 2023Article
- [Diagnostic accuracy of the automatic quantification software in patients with suspected COVID-19 of the Instituto Nacional de Cardiology Ignacio Chávez].Archivos de cardiologia de Mexico · 2022Article
- Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer.Frontiers in genetics · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) systems have become critical in support of decision-making. This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19. The ISI Web of Science, Cochrane Library, PubMed, Scopus, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched. We used the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess all included studies' quality and potential bias. A hierarchical receiver-operating characteristic summary (HSROC) curve and a summary receiver operating characteristic (SROC) curve have been implemented. The area under the curve (AUC) was computed to determine the diagnostic accuracy. Finally, 36 studies (a total of 39,246 image data) were selected for inclusion into the final meta-analysis. The pooled sensitivity for AI was 0.90 (95% CI, 0.90-0.91), specificity was 0.91 (95% CI, 0.90-0.92) and the AUC was 0.96 (95% CI, 0.91-0.98). For deep learning (DL) method, the pooled sensitivity was 0.90 (95% CI, 0.90-0.91), specificity was 0.88 (95% CI, 0.87-0.88) and the AUC was 0.96 (95% CI, 0.93-0.97). In case of machine learning (ML), the pooled sensitivity was 0.90 (95% CI, 0.90-0.91), specificity was 0.95 (95% CI, 0.94-0.95) and the AUC was 0.97 (95% CI, 0.96-0.99). AI in COVID-19 patients is useful in identifying symptoms of lung involvement. More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies.
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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.