Airway management failures remain among the most consequential events in anesthesia and emergency medicine — and conventional bedside screening tools have long been criticized for poor predictive accuracy. A comprehensive narrative review now maps how artificial intelligence is reshaping this high-stakes domain, from preoperative risk stratification to real-time procedural guidance and autonomous robotic intubation.
The review, covering literature through July 2025 and published in the British Journal of Anaesthesia, identifies several converging AI modalities showing clinical traction. Facial recognition algorithms and voice analysis tools are being applied to preoperative patient assessment, with multiparametric AI models demonstrating meaningfully higher positive predictive values than conventional single-metric tests such as the Mallampati score or thyromental distance. That said, the review is candid that low positive predictive value — the persistent Achilles' heel of difficult-airway prediction — has not been eliminated, only attenuated. On the procedural side, AI-enhanced videolaryngoscopy systems now offer real-time anatomical structure labeling, tracheal tube placement confirmation, and complication-reduction signals. In high-stress critical scenarios, AI cognitive support layers aim to counteract decision biases and enforce guideline adherence when human performance degrades under pressure. Experimental robotic intubation platforms guided by AI algorithms reportedly match expert-level performance in controlled settings.
This review arrives at a pivotal moment when AI is transitioning from proof-of-concept to clinical integration in procedural medicine. Several tensions deserve scrutiny: narrative reviews carry inherent selection bias and cannot establish effect sizes the way meta-analyses can, and most cited AI systems have been validated in controlled or simulated environments rather than diverse real-world patient populations. The deskilling risk flagged by the authors is arguably underappreciated — over-reliance on AI guidance during routine cases could erode the manual proficiency clinicians need precisely when AI systems fail. The algorithmic transparency problem is equally unresolved; black-box models that cannot explain their predictions are difficult to audit or correct after adverse events. Taken together, this is a confirmatory but genuinely useful synthesis signaling that AI in airway management is maturing, though robust prospective validation across heterogeneous patient cohorts remains the critical next step.