Evaluating the Impact of Artificial Intelligence-Driven Triage Systems on Emergency Care Efficiency in Resource-Limited Settings: A Scoping Review

Abstract

<title>Abstract</title> Background: Emergency departments (EDs) in resource-limited settings face persistent challenges including overcrowding, delayed triage, and workforce shortages. Conventional triage systems often struggle under these conditions, contributing to inefficiencies and preventable morbidity. Artificial intelligence (AI)-driven triage systems have emerged as innovative tools to enhance patient prioritization, clinical decision-making, and resource allocation. However, their real-world impact on emergency care efficiency, particularly within low- and middle-income countries (LMICs), remains insufficiently defined. Methods: This scoping review followed the Joanna Briggs Institute (JBI) methodology and adhered to PRISMA-ScR reporting standards. This review was registered with PROSPERO (1208548). Comprehensive searches were performed across PubMed, Scopus, Web of Science, IEEE Xplore, Cochrane Library, and Embase, supplemented by grey literature sources such as Google Scholar, WHO, and World Bank reports. Eligible studies examined AI-driven triage applications within emergency care settings, with relevance to LMICs or comparable resource-constrained environments. Data were charted and analyzed thematically to synthesize trends in design, implementation, and outcomes. Results: Forty-three studies met inclusion criteria, comprising 21 quantitative, 12 qualitative or mixed-methods, and 10 review articles. Most research originated from high-income countries, though studies from LMICs are increasing. AI triage systems consistently improved patient flow, reduced waiting times, and enhanced alignment between triage levels and clinical urgency. In resource-limited contexts, AI supported overburdened clinicians, optimized staffing, and improved patient safety. Key challenges included algorithmic bias, limited data infrastructure, insufficient external validation, and ethical concerns surrounding transparency and accountability. Conclusions: AI-driven triage systems demonstrate strong potential to enhance emergency care efficiency in resource-limited settings. However, context-specific evidence from LMICs remains limited. Future research should emphasize prospective validation, cost-effectiveness analysis, and ethical governance to ensure equitable and sustainable AI integration in emergency care.

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