Prehospital real-time AI for trauma mortality prediction: a multi-institutional and multi-national validation study

Early identification of high-risk trauma patients in the prehospital setting is crucial for optimizing resource allocation and improving survival. We developed and externally validated a real-time AI model predicting emergency room mortality using 21 prehospital variables. Model development and internal validation utilized the Korean Trauma Data Bank (KTDB; 204,189 patients), and external validation included four South Korean trauma centers (8,358 patients) and one Australian Level 1 center (3,578 patients). Our Prehospital-AI model, an ensemble of XGBoost, LightGBM, and random forest, achieved an AUROC of 0.923 (sensitivity: 0.780, specificity: 0.880) on the test set, outperforming the shock index (AUROC: 0.712). External validation yielded AUROCs of 0.925–0.956 across South Korean centers and 0.895 in the Australian center. Here we show that the Prehospital-AI model enables accurate, real-time risk assessment in the prehospital setting, outperforming traditional triage tools and improving trauma system efficiency. Nonetheless, additional multinational studies are warranted to further evaluate its generalizability across diverse trauma care systems. Early identification of high-risk trauma patients in prehospital settings is essential for effective triage and improved survival. Here the authors show that a real-time ensemble AI model accurately predicts emergency department mortality using only prehospital data, outperforming traditional triage tools.