Artificial Intelligence-Driven Triage in Pediatric Emergency Departments: Accuracy, Bias, and Impact on Clinical Outcomes: A Narrative Review

AI-driven triage presents a transformative opportunity to address persistent challenges in pediatric emergency care, from overcrowding and waiting times to human error and outcome disparities. This narrative review demonstrates that AI systems can achieve high accuracy in predicting critical outcomes, with pooled AUROCs of 0.87 for hospital admission, 0.93 for ICU admission, and 0.93 for mortality, significantly outperforming traditional triage scales, while observational studies report associations with improved efficiency, reduced triage errors, and enhanced resource allocation. However, publication bias favoring positive results affects the available evidence, and studies reporting no benefit or performance degradation exist. The promise of AI is tempered by significant challenges: performance varies across pediatric subgroups, the risks of perpetuating and amplifying bias remain inadequately addressed, and workflow integration and medico-legal liability require careful navigation. AI augments clinical judgment, guided by robust governance frameworks, fairness auditing, and human oversight for more equitable emergency care.