The applicability of artificial intelligence in managing emergency patients: An umbrella review
Abstract
BACKGROUND: 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.
AIM: This umbrella review aimed to synthesize existing review-level evidence on the applicability of artificial intelligence in managing emergency patients.
METHODS: 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.
RESULTS: 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…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.