Artificial Intelligence and Machine Learning-Based Triage Systems in Emergency Departments: A Systematic Review of Predictive Performance and Clinical Outcomes
Abstract
Emergency department (ED) triage systems are essential for prioritizing patients based on clinical urgency, yet traditional methods are subject to inter-observer variability and limited predictive accuracy. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to enhance triage decision-making. This systematic review synthesizes existing evidence on AI/ML-based triage systems in EDs, focusing on predictive performance and reported clinical outcomes, while critically appraising methodological quality and gaps affecting clinical applicability. A comprehensive search was conducted across PubMed, Scopus, CINAHL, IEEE Xplore, and Web of Science (2021-2026), supplemented by citation tracking. Studies developing or validating AI/ML models for ED triage and reporting quantitative performance metrics were included. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used for quality assessment. A narrative synthesis was performed due to substantial heterogeneity. Fourteen retrospective observational studies (2021-2026) from eight countries met the inclusion criteria (sample sizes: 657 to >2.6 million visits). Models included gradient-boosted trees, random forest, logistic regression, neural networks, natural language processing, and large language models. AUC-ROC ranged from 0.642 to 0.991 (highest for mortality (0.874-0.933) and pediatric critical illness (0.991)). However, calibration was reported in only three studies, external…
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.