ReviewDiagnostics (Basel, Switzerland)2022
A Literature Review on the Use of Artificial Intelligence for the Diagnosis of COVID-19 on CT and Chest X-ray.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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
14 citing papers in PubMed.
- Improving the accuracy of emergency department clinicians in detecting SARS-COV-2 on chest X-rays using a bespoke virtual training platform.BMC medical education · 2025Article
- Reinforced Collaborative-Competitive Representation for Biomedical Image Recognition.Interdisciplinary sciences, computational life sciences · 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
- Unlocking the potential of miRNAs in detecting pulmonary tuberculosis: prospects and pitfalls.Expert reviews in molecular medicine · 2024Review
- Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects.Heliyon · 2024Review
- Blazing the trail for innovative tuberculosis diagnostics.Infection · 2024Review
- Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.Scientific reports · 2023Article
- Development and validation of a deep learning model for multicategory pneumonia classification on chest computed tomography: a multicenter and multireader study.Quantitative imaging in medicine and surgery · 2023Article
- High-Resolution Network with Dynamic Convolution and Coordinate Attention for Classification of Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- 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
- The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis.Diagnostics (Basel, Switzerland) · 2022Review
- Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review.Journal of ophthalmic & vision researchReview
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A COVID-19 diagnosis is primarily determined by RT-PCR or rapid lateral-flow testing, although chest imaging has been shown to detect manifestations of the virus. This article reviews the role of imaging (CT and X-ray), in the diagnosis of COVID-19, focusing on the published studies that have applied artificial intelligence with the purpose of detecting COVID-19 or reaching a differential diagnosis between various respiratory infections. In this study, ArXiv, MedRxiv, PubMed, and Google Scholar were searched for studies using the criteria terms 'deep learning', 'artificial intelligence', 'medical imaging', 'COVID-19' and 'SARS-CoV-2'. The identified studies were assessed using a modified version of the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). Twenty studies fulfilled the inclusion criteria for this review. Out of those selected, 11 papers evaluated the use of artificial intelligence (AI) for chest X-ray and 12 for CT. The size of datasets ranged from 239 to 19,250 images, with sensitivities, specificities and AUCs ranging from 0.789-1.00, 0.843-1.00 and 0.850-1.00. While AI demonstrates excellent diagnostic potential, broader application of this method is hindered by the lack of relevant comparators in studies, sufficiently sized datasets, and independent testing.
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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.