September 10, 2025CureusOpen Access

Artificial Intelligence in Prehospital Emergency Care: Advancing Triage and Destination Decisions for Time-Critical Conditions

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AI models improved triage accuracy for trauma and stroke, significantly lowering undertriage rates to below 10%.
Machine learning algorithms achieved area under the curve values exceeding 0.98 for stroke prediction and 0.908 for critical care necessity.
Neural networks and random forest models reached an AUC of 0.88 in predicting prehospital acute respiratory distress syndrome.
While results are promising, most studies were retrospective, highlighting a need for focused prospective validations in diverse cohorts.

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Narrative review evaluates AI and ML models for prehospital triage and destination decisions, suggesting improved outcomes.

Zarei et al. (2025) studied this question.

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