Artificial intelligence in prehospital assessment of acute coronary syndrome: a scoping review

Acute coronary syndrome (ACS) remains a leading cause of morbidity and mortality worldwide, where timely diagnosis is critical. Prehospital assessment is challenging due to limited diagnostic resources and high clinical uncertainty. Artificial intelligence (AI) has emerged as a potential tool to support early diagnosis, risk stratification, and triage. This scoping review aimed to map the current evidence on AI applications in the prehospital assessment of suspected ACS and to identify existing knowledge gaps.

A scoping review was conducted in accordance with PRISMA-ScR guidelines. A comprehensive search of PubMed, Scopus, Web of Science, and Embase was performed up to May 2026. Studies evaluating AI-based models using prehospital data for ACS diagnosis, prediction, or triage were included.

19 studies involving 319,709 patients were included. AI-based models, particularly ECG-based deep learning and multimodal approaches, demonstrated promising diagnostic performance, with AUC values generally ranging from approximately 0.81 to 0.99, sensitivity from 73% to 94%, and specificity from 56% to 99%. These models improved sensitivity, reduced diagnostic variability, and enhanced triage efficiency in some settings. Risk prediction models showed moderate to good performance (AUC ~ 0.71-0.95) but were more variable. Emerging applications extended beyond diagnosis to risk stratification and decision support, including prediction of cardiogenic shock and need for revascularization.

AI shows considerable potential to improve prehospital assessment of suspected ACS, particularly through enhanced ECG interpretation and multimodal data integration. However, current evidence remains limited by methodological constraints. Future prospective, multicenter studies with standardized approaches are essential to support clinical implementation.