ARTIFICIAL INTELLIGENCE IN EMERGENCY DEPARTMENT TRIAGE: A NARRATIVE REVIEW OF ALGORITHMIC ACCURACY, RACIAL AND SOCIOECONOMIC DISPARITIES, AND THE INTEGRATION OF SOCIAL DETERMINANTS OF HEALTH - Danilczuk, Damian; Buszko, Jerzy; Dyś, Michał; Baran, Nicol; Bajkowska-Piterak, Marta; Piterak, Przemysław; Szlachta-Gubernat, Monika; Adamiec, Wiktor; Buż, Adrianna (International Journal of Innovative Technologies in Social Science, 2026)
Background: Emergency department (ED) crowding represents a growing global public health challenge. Limitations of conventional five-level triage scales — including interrater variability and documented racial and socioeconomic disparities — have driven increasing interest in artificial intelligence (AI) for triage optimization. Existing literature has largely examined algorithmic accuracy, fairness, and social determinants of health (SDOH) in isolation.Objective: This narrative review synthesizes evidence across three interconnected domains: algorithmic accuracy of AI triage models compared with conventional scales; racial and socioeconomic disparities and the mechanisms producing algorithmic bias; and the integration of SDOH into AI-driven triage.Methods: Peer-reviewed publications were retrieved from PubMed/MEDLINE, Embase, Scopus, Web of Science, and IEEE Xplore (January 2015 through March 2026), combining structured keyword-based searches across emergency care, AI methodology, equity, and SDOH.Results: AI models — particularly gradient-boosted ensembles and NLP-augmented architectures — consistently outperform ESI, MTS, and CTAS in predicting critical endpoints, with AUROC gains of 0.05–0.10. However, biased proxy variables, measurement artifacts (e.g., pulse oximetry), unrepresentative training data, and biased outcome labels can amplify existing disparities. SDOH integration offers potential for improving equity but faces challenges including Z-code underutilization, definitional heterogeneity, and stigmatization risk. Emerging frameworks — the EU AI Act, FDA guidance, WHO principles, and TRIPOD+AI — provide a scaffold for responsible deployment.Conclusions: Realizing AI's benefits while avoiding harm requires rigorous external validation, transparent subgroup reporting, explicit fairness evaluation, post-deployment monitoring, and participatory design.