Technological Integration in Triage With AI and Machine Learning Compared With Conventional Triage and Narrative Synthesis of Telehealth for Patient Prioritization in Emergency and Acute Care Settings: Systematic Review and Meta-Analysis

Conventional triage relies on clinician judgment and limited information available at first contact. AI and machine learning (ML) may improve early risk stratification, whereas tele-triage and patient-facing tools primarily support remote assessment, access, and care routing.

This study aimed to evaluate the diagnostic and prognostic performance of AI- and ML-assisted triage systems and summarize evidence on tele-triage, workflow, implementation, economic, and equity outcomes.

Comparative studies published between January 1, 2010, and December 31, 2025, evaluating AI-, ML-, natural language processing-, telehealth-, or digitally enhanced triage. At first, clinical contacts were included. PubMed/MEDLINE, CINAHL, Web of Science, Scopus, preprint servers, trial registries, and conference proceedings were searched. Screening and data extraction were performed independently in duplicate. Risk of bias was assessed using design-appropriate tools. Clinically comparable area under the receiver operating characteristic curve (AUC) estimates were synthesized using random-effects meta-analysis; heterogeneous diagnostic, operational, implementation, economic, and equity outcomes were summarized narratively. Exploratory scenario modeling projected effects per 10,000 emergency department (ED) visits.