Thu, Jun 26, 2025
Volume 13, Issue 2 (4-2025)
J. Pediatr. Rev 2025, 13(2): 107-112 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Sadeghinasab J, Atarodi Z. Investigating the Role of Artificial Intelligence in Pediatric Emergencies: A Narrative Review. J. Pediatr. Rev 2025; 13 (2) :107-112
URL: http://jpr.mazums.ac.ir/article-1-688-en.html
URL: http://jpr.mazums.ac.ir/article-1-688-en.html
1- Department of Pediatrics, School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran. , sadeghinasab.j@gmu.ac.ir
2- Department of Pediatrics, School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran.
2- Department of Pediatrics, School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran.
Abstract: (301 Views)
Background: Artificial intelligence (AI) and machine learning (ML) are transforming healthcare by enhancing diagnostic accuracy, predictive capabilities, and clinical decision-making. In pediatric emergencies, where rapid diagnosis and treatment are critical, AI offers unique advantages, including addressing challenges like complex comorbidities and limited access to specialized care.
Objectives: This narrative review examines the current applications, benefits, and limitations of artificial intelligence in pediatric emergencies, focusing on diagnostic support, predictive analytics, clinical decision-making, and medical imaging.
Methods: A systematic review of articles (2013–2023) from PubMed, Scopus, and Google Scholar was conducted using keywords such as “artificial intelligence” and “pediatric emergencies.” Relevant studies were identified and analyzed for AI’s clinical applications, outcomes, and impact.
Results: Diagnostic support has seen significant advances with AI models improving the early detection of conditions like pediatric sepsis, offering better accuracy and timeliness compared to traditional methods. In predictive analytics, AI tools forecast clinical deterioration in pediatric patients, enabling preemptive interventions in critical care settings. Clinical decision support systems (CDSSs) powered by AI assist clinicians with real-time recommendations, reducing errors and improving adherence to guidelines. In medical imaging, AI enhances the interpretation of imaging studies such as x-rays and computed tomography scans, expediting the diagnosis of fractures, hemorrhages, and other conditions.
Conclusions: AI holds significant promise in transforming pediatric emergency care by enhancing accuracy, efficiency, and outcomes. However, addressing challenges related to data quality, ethical considerations, and workflow integration is critical to unlocking its full potential. Collaborative efforts between clinicians, data scientists, and policymakers will be essential for successfully implementing AI in this field.
Objectives: This narrative review examines the current applications, benefits, and limitations of artificial intelligence in pediatric emergencies, focusing on diagnostic support, predictive analytics, clinical decision-making, and medical imaging.
Methods: A systematic review of articles (2013–2023) from PubMed, Scopus, and Google Scholar was conducted using keywords such as “artificial intelligence” and “pediatric emergencies.” Relevant studies were identified and analyzed for AI’s clinical applications, outcomes, and impact.
Results: Diagnostic support has seen significant advances with AI models improving the early detection of conditions like pediatric sepsis, offering better accuracy and timeliness compared to traditional methods. In predictive analytics, AI tools forecast clinical deterioration in pediatric patients, enabling preemptive interventions in critical care settings. Clinical decision support systems (CDSSs) powered by AI assist clinicians with real-time recommendations, reducing errors and improving adherence to guidelines. In medical imaging, AI enhances the interpretation of imaging studies such as x-rays and computed tomography scans, expediting the diagnosis of fractures, hemorrhages, and other conditions.
Conclusions: AI holds significant promise in transforming pediatric emergency care by enhancing accuracy, efficiency, and outcomes. However, addressing challenges related to data quality, ethical considerations, and workflow integration is critical to unlocking its full potential. Collaborative efforts between clinicians, data scientists, and policymakers will be essential for successfully implementing AI in this field.
Keywords: Artificial intelligence (AI), Pediatric emergency, Machine learning, Clinical decision support, Medical imaging
Type of Study: Narrative Review |
Subject:
Pediatric Intensivist
Received: 2024/10/12 | Accepted: 2025/01/1 | Published: 2025/04/1
Received: 2024/10/12 | Accepted: 2025/01/1 | Published: 2025/04/1
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Copyright © The Author(s);
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-By-NC),
which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Contact Information
