Artificial Intelligence Applications In Pediatric Emergency Care Decision-Making And Diagnostics

Abstract

Background: Pediatric emergency departments need fast and precise clinical decision-making to improve outcomes and minimize diagnostic delays. Artificial Intelligence (AI) has been identified as a promising technology to support emergency care through predictive analytics, clinical decision support, and automated diagnostic systems.  Objective: The study explores the impact of AI applications on improving decision-making and diagnostic accuracy in pediatric emergency care settings.  Methodology: A thorough review and analytical evaluation of AI based technologies including machine learning, deep learning, natural language processing and predictive modeling was conducted using recent pediatric emergency healthcare studies. The performance indicators such as diagnostic accuracy, sensitivity, specificity and efficiency of decision support were evaluated.  Findings: The analysis showed that the diagnostic accuracy of AI-assisted diagnostic systems ranged from 88% to 96%, and predictive models improved early risk identification by roughly 25–35%. AI-based triage systems showed a reduction in patient evaluation time of almost 30%, and improved consistency in clinical decision-making. These technologies were very promising in identifying critical conditions, directing treatment priorities and reducing the risk of medical errors.  Conclusion: AI applications advance pediatric emergency care significantly by enhancing the accuracy of diagnosis, the speed of clinical decision-making and the allocation of resources. Ongoing incorporation of AI technologies has the potential to make emergency health care services safer, more efficient and more focused on patients.