ARTIFICIAL INTELLIGENCE IN EMERGENCY DEPARTMENT TRIAGE: A NARRATIVE REVIEW OF ALGORITHMIC ACCURACY, RACIAL AND SOCIOECONOMIC DISPARITIES, AND THE INTEGRATION OF SOCIAL DETERMINANTS OF HEALTH

Damian Danilczuk; Jerzy Buszko; Michał Dyś; Nicol Baran; Marta Bajkowska-Piterak; Przemysław Piterak; Monika Szlachta-Gubernat; Wiktor Adamiec; Adrianna Buż

doi:10.31435/ijitss.2(50).2026.5675

Authors

Damian Danilczuk John Paul II Municipal Hospital in Rzeszów, Rycerska 4, 35-241 Rzeszów, Poland https://orcid.org/0009-0005-5755-1115
Jerzy Buszko Medical Care Center in Jarosław, 3-go Maja 70, 37-500 Jarosław, Poland https://orcid.org/0009-0008-2708-1222
Michał Dyś Health Care Complex in Dębica, Krakowska 91, 39-200 Dębica, Poland https://orcid.org/0009-0000-2644-7626
Nicol Baran Medical Center in Łańcut, Ignacego Paderewskiego 5, 37-100 Łańcut, Poland https://orcid.org/0009-0003-3607-6744
Marta Bajkowska-Piterak Medical Center in Łańcut, Ignacego Paderewskiego 5, 37-100 Łańcut, Poland https://orcid.org/0009-0000-6689-0788
Przemysław Piterak Hospital of the Ministry of Interior and Administration in Rzeszów, Krakowska 16, 35-111 Rzeszów, Poland https://orcid.org/0009-0002-3553-7353
Monika Szlachta-Gubernat Hospital of the Ministry of Interior and Administration in Rzeszów, Krakowska 16, 35-111 Rzeszów, Poland https://orcid.org/0009-0005-6994-9761
Wiktor Adamiec John Paul II Municipal Hospital in Rzeszów, Rycerska 4, 35-241 Rzeszów, Poland https://orcid.org/0009-0002-3322-5479
Adrianna Buż John Paul II Municipal Hospital in Rzeszów, Rycerska 4, 35-241 Rzeszów, Poland https://orcid.org/0009-0001-8202-5628

DOI:

https://doi.org/10.31435/ijitss.2(50).2026.5675

Keywords:

Artificial Intelligence; Emergency Triage; Algorithmic Bias; Health Equity; Social Determinants of Health

Abstract

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.

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