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Artificial intelligence in airway management: a narrative review
Massimiliano Sorbello1, Luigi La Via2, Daniele S Paternò3
1Department of Medicine and Surgery, Kore University, Enna, Italy; Department of Anaesthesia and Intensive Care, "Giovanni Paolo II" Hospital, ASP 7 Ragusa, Italy.
British Journal of Anaesthesia
|February 5, 2026
Summary
Artificial intelligence (AI) enhances airway management by improving difficult airway prediction and videolaryngoscopy guidance. AI offers cognitive support and personalized education, but human expertise remains essential, necessitating careful validation and ethical frameworks.
Area of Science:
- Anesthesiology
- Medical Informatics
- Artificial Intelligence in Medicine
Background:
- Airway management is critical in patient care.
- Predicting difficult airways and optimizing procedures remain challenges.
- Cognitive support during critical events is vital for patient safety.
Purpose of the Study:
- To review current and emerging artificial intelligence (AI) applications in airway management.
- To evaluate AI's potential in predicting difficult airways, enhancing videolaryngoscopy, and providing cognitive support.
- To discuss the challenges and future directions of AI in airway management.
Main Methods:
- Structured literature search on AI in airway management (completed July 2025).
- Analysis of AI applications in prediction, procedural guidance, cognitive support, education, and robotics.
- Review of AI model performance, including positive predictive values.
Main Results:
- AI shows promise in predicting difficult airways with improved, though imperfect, positive predictive values.
- AI-powered videolaryngoscopy systems offer real-time guidance and verification, potentially reducing complications.
- AI provides cognitive support, enhances medical education via virtual reality, and shows potential in robotic intubation training.
Conclusions:
- AI can significantly complement human expertise in airway management, improving prediction and procedural outcomes.
- Challenges include clinician deskilling, algorithmic transparency, and the need for robust validation.
- Human-machine collaboration, ethical frameworks, and regulatory oversight are crucial for safe AI implementation in healthcare.
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