Medical Journal of The Islamic Republic of Iran (MJIRI)
Iran University of Medical Sciences
Thu, Jul 16, 2026
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Volume 40, Issue 1 (1-2026)
Med J Islam Repub Iran 2026 |
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Shakoori M, Esmaili F, Abbariki E, Heidaranlu E. The Role of Artificial Intelligence in Enhancing Diagnostic and Triage Accuracy in Hospital Emergency Departments: A Systematic Review. Med J Islam Repub Iran 2026; 40 (1) :498-504
URL: http://mjiri.iums.ac.ir/article-1-10362-en.html
URL: http://mjiri.iums.ac.ir/article-1-10362-en.html
Nursing Care Research Center, Clinical Sciences Institute, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran , Heidaranlu@bmsu.ac.ir
Abstract: (79 Views)
Background: Emergency departments face challenges of overcrowding and human error. This systematic review evaluates the effectiveness of artificial intelligence (AI) in improving triage and diagnostic accuracy in emergency settings.
Methods: This systematic review was conducted by searching PubMed, Scopus, Web of Science, and Google Scholar until September 2025. Inclusion criteria comprised original studies reporting at least one quantitative performance metric. Study quality was assessed using NOS and QUADAS-2. Data were synthesized narratively.
Results: AI models outperformed traditional triage systems (AUC often >0.90). In clinical interpretation of AUC, its limitations in imbalanced datasets should be considered, and metrics such as PPV, NPV, and AUPRC should also be reported. For instance, despite an acceptable AUC, ChatGPT 4.0 led to significant over-triage due to low specificity (18.2%). However, risks of algorithmic bias and challenges of integrating AI with real-world clinical workflows remain major implementation barriers. User resistance and the need for continuous ethical oversight were also identified.
Conclusion: AI holds potential to improve emergency triage, but current evidence is largely based on retrospective single-center studies. Clinical implementation should be gradual, starting in high-risk domains (e.g., sepsis screening), and supported by continuous ethical oversight.
Methods: This systematic review was conducted by searching PubMed, Scopus, Web of Science, and Google Scholar until September 2025. Inclusion criteria comprised original studies reporting at least one quantitative performance metric. Study quality was assessed using NOS and QUADAS-2. Data were synthesized narratively.
Results: AI models outperformed traditional triage systems (AUC often >0.90). In clinical interpretation of AUC, its limitations in imbalanced datasets should be considered, and metrics such as PPV, NPV, and AUPRC should also be reported. For instance, despite an acceptable AUC, ChatGPT 4.0 led to significant over-triage due to low specificity (18.2%). However, risks of algorithmic bias and challenges of integrating AI with real-world clinical workflows remain major implementation barriers. User resistance and the need for continuous ethical oversight were also identified.
Conclusion: AI holds potential to improve emergency triage, but current evidence is largely based on retrospective single-center studies. Clinical implementation should be gradual, starting in high-risk domains (e.g., sepsis screening), and supported by continuous ethical oversight.
: Systematic Review |
Subject:
Emergency Medicine
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