Artificial Intelligence and Advanced Technologies in Pediatric Airway Management: Transforming Emergency Care

Pediatric airway management in the Emergency Department is a critical intervention with high stakes and significant complication rates, including adverse events in 16–25% of intubations and first-attempt success rates of approximately 75%. This review examines the application of advanced digital technologies—artificial intelligence (AI), machine learning (ML), computer vision, deep learning, and predictive analytics—to address key gaps: unreliable prediction of difficult pediatric airways (with first-pass failure rates of 30–40%), variability in device selection and technique, and the absence of robust real-time decision support for clinicians. Emerging AI tools demonstrate promise across three phases of airway management. For prediction, machine learning models analyzing patient data can identify difficult airways with accuracy approaching that of expert clinicians. During performance, deep learning algorithms applied to video laryngoscopy automatically recognize anatomical landmarks (e.g., vocal cords) and guide laryngoscope positioning, improving visualization and potentially first-attempt success. For monitoring, AI-based systems analyze real-time physiological signals - including heart rate, oxygen saturation, and respiratory patterns - to detect subtle signs of airway difficulty or patient decompensation, showing improved hypoxia detection compared to standard monitoring. Integrating AI and advanced technologies could enable a future of “precision airway management,” tailoring interventions to individual patient anatomy, physiology, and risk factors to reduce failed intubations and complications. Realizing this potential requires ensuring equitable access to avoid disparities between centers, mitigating algorithmic bias, and fundamentally incorporating AI education—covering advanced airway technologies and human-AI collaboration—into Pediatric Emergency Medicine and anesthesia training programs.