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Systematic reviews

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Artificial intelligence for the triage of COVID-19 patients at the emergency department: a systematic review

  • Pablo Redruello-Guerrero1
  • Carmen Jiménez-Gutiérrez2
  • Antonio Jesús Láinez Ramos-Bossini3,*,
  • Paula María Jiménez-Gutiérrez4
  • Mario Rivera-Izquierdo5,6
  • José Manuel Benítez Sánchez7

1Faculty of Medicine, University of Granada, 18016 Granada, Spain

2Department of Nursing, University of Granada, 18016 Granada, Spain

3Department of Radiology, Virgen de las Nieves University Hospital, 18016 Granada, Spain

4Service of Anesthesiology, Resuscitation and Pain Therapeutics, Hospital Universitario Virgen de las Nieves, 18014 Granada, Spain

5Service of Preventive Medicine and Public Health, Hospital Universitario Clínico San Cecilio, 18016 Granada, Spain

6Department of Preventive Medicine and Public Health, University of Granada, 18016 Granada, Spain

7Department of Computer Science and Artificial Intelligence, E.T.S. Ingenieria Informática, University of Granada, 18071 Granada, Spain

DOI: 10.22514/sv.2022.069 Vol.18,Issue 6,November 2022 pp.17-26

Submitted: 09 February 2022 Accepted: 29 March 2022

Published: 08 November 2022

*Corresponding Author(s): Antonio Jesús Láinez Ramos-Bossini E-mail:


The aim of this article is to systematically analyze the available literature on the efficacy and validity of artificial intelligence (AI) applied to medical imaging techniques in the triage of patients with suspected or confirmed coronavirus disease 2019 (COVID-19) in Emergency Departments (EDs). A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. Medline, Web of Science, and Scopus were searched to identify observational studies evaluating the efficacy of AI methods in the diagnosis and prognosis of COVID-19 using medical imaging. The main characteristics of the selected studies were extracted by two independent researchers and were formally assessed in terms of methodological quality using the Newcastle-Ottawa scale. A total of 11 studies, including 14,499 patients, met inclusion criteria. The quality of the studies was medium to high. Overall, the diagnostic yield of the AI techniques compared to a gold standard was high, with sensitivity and specificity values ranging from 79% to 98% and from 70%to 93%, respectively. The methodological approaches and imaging datasets were highly heterogeneous among studies. In conclusion, AI methods significantly boost the diagnostic yield of medical imaging in the triage of COVID-19 patients in the ED. However, there are significant limitations that should be overcome in future studies, particularly regarding the heterogeneity and limited amount of available data to train AI models.


COVID-19; Diagnostic imaging; Radiology; Artificial intelligence; Machine learning; Sensitivity; Specificity; Validity; Emergency

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Pablo Redruello-Guerrero,Carmen Jiménez-Gutiérrez,Antonio Jesús Láinez Ramos-Bossini,Paula María Jiménez-Gutiérrez,Mario Rivera-Izquierdo,José Manuel Benítez Sánchez. Artificial intelligence for the triage of COVID-19 patients at the emergency department: a systematic review. Signa Vitae. 2022. 18(6);17-26.


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