From decision support to clinical integration: A scoping review of artificial intelligence in prehospital airway management

Fangfang Bai, Wenjuan Qiu, Xiaoting Zhu, Yanghui Feng

TL;DR: Artificial intelligence shows considerable potential to support prehospital airway management across multiple stages of care, but current evidence remains exploratory and is limited by methodological constraints, lack of prospective validation, and insufficient integration with clinical workflows.

BACKGROUND Airway management is a critical component of prehospital emergency care, where rapid decision-making and procedural accuracy are essential for patient survival. In recent years, artificial intelligence has emerged as a promising tool. However, the current landscape and translational readiness of artificial intelligence applications in prehospital airway management remain unclear. OBJECTIVE This scoping review aimed to synthesize existing evidence on the applications of artificial intelligence in prehospital airway management and to identify current research gaps and challenges for clinical integration. METHODS This review followed the PRISMA-ScR guidelines. A systematic search of PubMed, Web of Science, and EBSCOhost was conducted through February 2026. Two reviewers independently screened studies and extracted data on study characteristics, artificial intelligence methods, clinical applications, and performance metrics. RESULTS Nine studies published between 2020 and 2026 were included. artificial intelligence applications were categorized into four functional domains: predictive modeling for airway intervention and triage, physiological signal monitoring, natural language processing for clinical documentation analysis, and computer vision for anatomical recognition during intubation. Most studies focused on prediction before airway intervention, while relatively few addressed procedural assistance or post-intubation monitoring. Model performance was generally strong, with reported AUC values ranging from 0.867 to 0.960. However, all studies relied on retrospective data and internal validation; only one study conducted external validation, and none reported prospective trials or fairness assessments. CONCLUSIONS Artificial intelligence shows considerable potential to support prehospital airway management across multiple stages of care. Nevertheless, current evidence remains exploratory and is limited by methodological constraints, lack of prospective validation, and insufficient integration with clinical workflows. Future research should prioritize multimodal data integration, external validation, and prospective implementation studies to facilitate the safe and effective translation of artificial intelligence into real-world prehospital practice. Protocol registered number: PROSPERO (CRD42026132386).