Medical Journal of the Islamic Republic Of Iran

en Diagnostic Accuracy of Artificial Intelligence in Predicting Admission Status, Intensive Care Requirements, and Mortality in the Emergency Department: A Systematic Review and Meta-Analysis Emergency Medicine Emergency Medicine Meta-analysis Article 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. Artificial Intelligence, Machine Learning, Deep Learning, Emergency Department, Diagnostic Accuracy 130 138 http://mjiri.iums.ac.ir/browse.php?a_code=A-10-10100-1&slc_lang=en&sid=1 Seyed Mohammad Hosseini Kasnavieh mhoseini1346@mailfa.com 200319475328460096660 200319475328460096660 No Department of Emergency Medicine, School of Medicine, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran Seyed Hossien Shaker shaker.h@iums.ac.ir 200319475328460096661 200319475328460096661 No Department of Emergency Medicine, School of Medicine, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran Maryam Milanifard dr.milanifardmaryam@gmail.com 200319475328460096662 200319475328460096662 No 3. Trauma and Injury Research Center, Student Research Committee, Iran University of Medical Sciences, Tehran, Iran Roxana Hessam hessam.r@iums.ac.ir 200319475328460096663 200319475328460096663 Yes Department of Emergency Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran Ali Saghandian Tousi saghandian@gmail.com 200319475328460096664 0009-0003-3439-4546 No Department of Emergency Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran Marjan Ghadesi marjanghadesi@gmail.com 200319475328460096665 0009-0009-8047-388X No Department of Emergency Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran