Artificial Intelligence in the Golden Hour: A scoping review of prehospital trauma triage and implementation feasibility in LMICs.
Artificial Intelligence route optimization linked to response times falling from 162 to 13 minutes
Artificial Intelligence in the Golden Hour: A scoping review of prehospital trauma triage and implementation feasibility in LMICs.
The Golden Hour of trauma care in Low- and Middle-Income Countries (Low- and Middle-Income Countries) is routinely compromised by systemic deficits, including unmapped infrastructure, chronic traffic congestion, and a critical scarcity of diagnostic tools.
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.
response times dropped dramatically, faster arrival to trauma patients
(1) Clinical Feasibility: Machine Learning (Machine Learning) 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.
(3) Technical Feasibility: Edge-AI hardware and Natural Language Processing (Natural Language Processing) for informal audio transcription achieved 95% accuracy in connectivity-starved and noisy environments.
Artificial Intelligence in Low- and Middle-Income Countries 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 Artificial Intelligence maturity index of 52 out of 100.
Achieving an Artificial Intelligence-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.