SynthesisEuropean journal of radiology2021
Artificial intelligence for imaging-based COVID-19 detection: Systematic review comparing added value of AI versus human readers.
Synthesis in European journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Diagnostic accuracy of AI in chest radiography for pneumonia and lung cancer: A meta-analysis.European journal of radiology open · 2025Article
- Utilizing artificial intelligence as an arbitrary tool in managing difficult COVID-19 cases in critical care medicine.World journal of critical care medicine · 2025Review
- An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics.Archives of public health = Archives belges de sante publique · 2025Article
- The extended impact of the COVID-19 pandemic on medical imaging case volumes: a retrospective study.PeerJ · 2025Article
- Optimising strategies for artificial intelligence-assisted classification of viral pneumonias on CT imaging: a comparative study of selective and default approaches.Polish journal of radiology · 2025Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models.Journal of imaging · 2023Article
- Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.Cureus · 2023Review
- Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches.European radiology experimental · 2023Article
- Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.Tomography (Ann Arbor, Mich.) · 2022Article
- COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods.Bioengineering (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeA growing number of studies have examined whether Artificial Intelligence (AI) systems can support imaging-based diagnosis of COVID-19-caused pneumonia, including both gains in diagnostic performance and speed. However, what is currently missing is a combined appreciation of studies comparing human readers and AI.
methodsWe followed PRISMA-DTA guidelines for our systematic review, searching EMBASE, PUBMED and Scopus databases. To gain insights into the potential value of AI methods, we focused on studies comparing the performance of human readers versus AI models or versus AI-supported human readings.
resultsOur search identified 1270 studies, of which 12 fulfilled specific selection criteria. Concerning diagnostic performance, in testing datasets reported sensitivity was 42-100% (human readers, n = 9 studies), 60-95% (AI systems, n = 10) and 81-98% (AI-supported readers, n = 3), whilst reported specificity was 26-100% (human readers, n = 8), 61-96% (AI systems, n = 10) and 78-99% (AI-supported readings, n = 2). One study highlighted the potential of AI-supported readings for the assessment of lung lesion burden changes, whilst two studies indicated potential time savings for detection with AI.
conclusionsOur review indicates that AI systems or AI-supported human readings show less performance variability (interquartile range) in general, and may support the differentiation of COVID-19 pneumonia from other forms of pneumonia when used in high-prevalence and symptomatic populations. However, inconsistencies related to study design, reporting of data, areas of risk of bias, as well as limitations of statistical analyses complicate clear conclusions. We therefore support efforts for developing critical elements of study design when assessing the value of AI for diagnostic imaging.
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