The applicability of artificial intelligence in managing emergency patients: An umbrella review

Artificial intelligence (AI) is increasingly reshaping emergency medicine and nursing by enabling faster, more accurate, and scalable decision-making. In high-pressure settings such as emergency departments (EDs), AI technologies have shown promise in improving triage, diagnostics, clinical decision-making, and operational efficiency. Despite this potential, the current body of evidence remains fragmented, with a lack of comprehensive synthesis across diverse AI applications.

This umbrella review aimed to synthesize existing review-level evidence on the applicability of artificial intelligence in managing emergency patients.

A systematic umbrella review was conducted following PRISMA guidelines. Systematic reviews, scoping reviews, and narrative syntheses focusing on AI in emergency settings were identified through comprehensive searches in five major databases. The search covered studies published between January 2013 and March 2025. Data were extracted on AI types, clinical focus areas, implementation strategies, outcomes, and barriers. The Joanna Briggs Institute (JBI) checklist was used for quality assessment, and findings were synthesized thematically.

A total of 24 eligible reviews were included. The analysis revealed AI's significant impact across four major domains: triage and risk stratification, diagnostic support, clinical decision-making, and workflow optimization. Tools such as machine learning and deep learning improved diagnostic accuracy, decision consistency, and patient flow. However, barriers such as interoperability issues, lack of explainability, data privacy concerns, and legal ambiguity were consistently reported.

AI holds transformative potential in emergency patient management. While clinical benefits are clear, widespread adoption depends on addressing technical, ethical, and regulatory challenges. Future efforts should focus on explainable, validated, and user-integrated AI systems to ensure responsible and equitable implementation in emergency care.