Artificial intelligence in airway management: a narrative review

Abstract

This narrative review examines artificial intelligence (AI) applications in airway management through a structured literature search completed in July 2025. AI shows promise in predicting difficult airways through facial recognition, voice analysis, and multiparametric assessment. AI models demonstrate improved positive predictive values compared with conventional bedside tests, though prediction remains imperfect. The fundamental challenge of low positive predictive values persists, albeit at reduced levels. For videolaryngoscopy, AI-powered systems provide real-time structure identification, procedural guidance, tracheal tube placement verification, and potentially fewer complications. AI also offers cognitive support during critical scenarios by mitigating decision biases, providing intelligent alarms, and ensuring guideline adherence when human performance might be compromised by stress. In education, AI-enhanced virtual reality simulations create realistic practice environments with personalised feedback tailored to learners' needs. Experimental robotic intubation systems guided by AI algorithms demonstrate performance comparable with experts in controlled settings, though primarily for training and remote applications. Despite these advances, AI should complement rather than replace human expertise. Important challenges remain, including the risk of clinician deskilling, the 'black box' nature of some algorithms limiting transparency, and the need for robust validation before widespread implementation. The ideal approach involves human-machine collaboration where AI compensates for cognitive limitations while physicians contribute clinical judgement, context awareness, and empathy. As AI continues evolving, appropriate ethical frameworks and regulatory oversight must be developed in parallel to ensure these technologies enhance patient safety while preserving the essential human element of healthcare.

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