Artificial Intelligence in the Golden Hour: A scoping review of prehospital trauma triage and implementation feasibility in LMICs
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Published on: November 27, 2013
Kholisah Widiyawati1, Fitrio Deviantony2
1Department of Nursing, Faculty of Health Sciences, Universitas Brawijaya, Malang, East Java, Indonesia.
Background:
The Golden Hour of trauma care in Low- and Middle-Income Countries (LMICs) is routinely compromised by systemic deficits, including unmapped infrastructure, chronic traffic congestion, and a critical scarcity of diagnostic tools. While high-income countries utilize Artificial Intelligence (AI) to optimize mature systems, AI in the Global South acts as a structural substitute to leapfrog foundational barriers. This scoping review maps AI applications in LMIC prehospital care and evaluates their implementation feasibility.
Methods:
Following PRISMA-ScR guidelines, a systematic search was performed across PubMed, ScienceDirect, Scopus, IRIS WHO, SciELO, and snowballing for the period of May 2020 to April 2026. To prioritize resource-constrained settings, high-maturity AI nations were excluded from primary synthesis. Seventeen primary studies were definitively identified and synthesized into three feasibility domains.
Results:
(1) Clinical Feasibility: Machine Learning (ML) models such as Random Forest and LightGBM consistently outperformed traditional manual scores like the Kampala Trauma Score, achieving an AUC of 0.91 to 0.94. Bayesian models in Tanzania successfully utilized prehospital delay variables to predict mortality. (2) Operational Feasibility: Digital platforms like Flare in Kenya navigate uncharted roads using ride-hailing logic, while robust optimization in Bangladesh resists extreme traffic chaos, successfully reducing average response times from 162 to 13 min. (3) Technical Feasibility: Edge-AI hardware and Natural Language Processing (NLP) for informal audio transcription achieved 95% accuracy in connectivity-starved and noisy environments.
Conclusion:
AI in LMICs serves as a vital diagnostic safety net rather than merely an optimization tool. However, a decisive readiness gap persists; for instance, Indonesia currently holds a health AI maturity index of 52 out of 100. Achieving an AI-enabled Golden Hour requires a strategic roadmap focused on sovereign national data registries and legal readiness to protect these leapfrog innovations from a current policy vacuum regarding liability.
