Medical Journal of The Islamic Republic of Iran (MJIRI)
Iran University of Medical Sciences
Tue, Apr 28, 2026
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Volume 40, Issue 1 (1-2026)
Med J Islam Repub Iran 2026 |
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Hosseini Kasnavieh S M, Shaker S H, Milanifard M, Hessam R, Saghandian Tousi A, Ghadesi M. Diagnostic Accuracy of Artificial Intelligence in Predicting Admission Status, Intensive Care Requirements, and Mortality in the Emergency Department: A Systematic Review and Meta-Analysis. Med J Islam Repub Iran 2026; 40 (1) :130-138
URL: http://mjiri.iums.ac.ir/article-1-10175-en.html
URL: http://mjiri.iums.ac.ir/article-1-10175-en.html
Seyed Mohammad Hosseini Kasnavieh 
, Seyed Hossien Shaker , Maryam Milanifard , Roxana Hessam , Ali Saghandian Tousi , Marjan Ghadesi
, Seyed Hossien Shaker , Maryam Milanifard , Roxana Hessam , Ali Saghandian Tousi , Marjan Ghadesi
Department of Emergency Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran , hessam.r@iums.ac.ir
Abstract: (21 Views)
Background: Predicting patient outcomes in the emergency department is crucial for effective resource management and enhancing the quality of care. Recent advancements in artificial intelligence have facilitated accurate predictions of patient outcomes; however, consistent evidence regarding the diagnostic accuracy of these models in the emergency department remains limited. Therefore, the aim of the present study was to evaluate the diagnostic accuracy of artificial intelligence in predicting admission status, intensive care requirements, and in-hospital mortality in the emergency department.
Methods: In the present study, the PubMed, Embase, Cochrane Library, and Web of Science databases were searched from January 2020 to November 2025 using targeted keywords. A total of 34 relevant studies were included in the analysis. Meta-analysis was conducted using Stata v.17 software.
Results: The overall diagnostic sensitivity and specificity of the models for predicting admission were 0.77 (95% CI, 0.60-0.93) and 0.78 (95% CI, 0.62-0.95), respectively. For critical care, sensitivity was 0.85 (95% CI, 0.57-1.00) and specificity was 0.86 (95% CI, 0.57-1.00). For mortality, sensitivity was 0.83 (95% CI, 0.60-1.00) and specificity was 0.90 (95% CI, 0.67-1.00).
Conclusion: Artificial intelligence models, encompassing both machine learning and deep learning, serve as effective tools for predicting the conditions of emergency patients. The findings indicate that AI holds significant potential to enhance clinical decision-making within the emergency department.
Methods: In the present study, the PubMed, Embase, Cochrane Library, and Web of Science databases were searched from January 2020 to November 2025 using targeted keywords. A total of 34 relevant studies were included in the analysis. Meta-analysis was conducted using Stata v.17 software.
Results: The overall diagnostic sensitivity and specificity of the models for predicting admission were 0.77 (95% CI, 0.60-0.93) and 0.78 (95% CI, 0.62-0.95), respectively. For critical care, sensitivity was 0.85 (95% CI, 0.57-1.00) and specificity was 0.86 (95% CI, 0.57-1.00). For mortality, sensitivity was 0.83 (95% CI, 0.60-1.00) and specificity was 0.90 (95% CI, 0.67-1.00).
Conclusion: Artificial intelligence models, encompassing both machine learning and deep learning, serve as effective tools for predicting the conditions of emergency patients. The findings indicate that AI holds significant potential to enhance clinical decision-making within the emergency department.
Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Emergency Department, Diagnostic Accuracy
Type of Study: Meta-analysis Article |
Subject:
Emergency Medicine
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