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.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: Twenty-seven studies were included, comprising 13 studies in the quantitative evidence stream and 14 in the narrative synthesis. Four adult ED studies evaluating critical care or deterioration outcomes demonstrated high pooled discrimination for AI-assisted triage (AUC 0.874; 95% CI 0.845-0.898; I²=94.4%; 95% prediction interval 0.803-0.922). Four studies reported directly extractable within-study comparisons favoring AI-assisted models (AUC differences of 0.097, 0.120, 0.108, and 0.065), although these could not be pooled because paired variance estimates were unavailable. Admission/disposition studies reported AI-model AUCs ranging from 0.80 to 0.93 and were summarized descriptively because extreme heterogeneity precluded meaningful pooling. Two pediatric studies (AUCs of 0.85-0.99) were summarized descriptively. Prediction-model studies were primarily limited by deficiencies in model analysis, calibration reporting, and external validation. Narrative evidence suggested improved front-door operational metrics with tele-triage, including reported reductions of 15%-40% in time to clinical assessment and 2-8 percentage points in left-without-being-seen rates, whereas patient-facing symptom checkers generally favored sensitivity over specificity. Exploratory scenario modeling illustrated that, under specified prevalence and threshold assumptions, AI-assisted triage could be associated with 12-38 fewer missed critical illness cases and 45-120 additional false-positive escalations per 10,000 ED visits. Economic and equity findings were assumption-dependent projections rather than observed clinical, cost-effectiveness, or equity outcomes. CONCLUSIONS: AI- and ML-assisted triage models demonstrate promising discrimination for adult ED risk stratification; however, AUC-based discrimination does not establish superiority at clinically relevant decision thresholds, improved patient outcomes, or readiness for clinical implementation. Interpretation is limited by substantial heterogeneity, incomplete threshold-level performance and calibration reporting, limited external validation, and sparse pediatric evidence. Prospective multicenter validation, workflow assessment, subgroup calibration and equity auditing, and formal economic evaluation are required before routine implementation.