Beyond Mallampati: From Universal Bedside Tests to Precision Airway Risk Stratification – A Narrative Review

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Abstract
Airway management remains one of the few domains of anaesthetic practice still governed by universal, dichotomous screening thresholds applied to profoundly heterogeneous populations. Conventional bedside tests — the modified Mallampati test, thyromental distance and the upper lip bite test — retain modest and inconsistent diagnostic accuracy, leaving the majority of difficult intubations unanticipated while generating false alarms that drive resource-intensive contingency plans. This narrative review reframes preoperative airway evaluation as a problem of individualized risk stratification rather than population screening. We first summarize the performance limits of classical bedside predictors and the reasons for their ceiling effect, including low target prevalence, inter-observer variability, spectrum bias and heterogeneous outcome definitions. We then examine the data streams reshaping the field: point-of-care upper airway ultrasound, whose parameters provide operator-accessible and objective anatomical information; cross-sectional and craniofacial imaging; and machine-learning models based on clinical variables, facial images and voice analysis. For each we discuss accuracy, reproducibility, validation status and barriers to adoption. We then propose a phenotype-oriented framework in which assessment intensity is matched to pre-test probability, with dedicated pathways for obesity and obstructive sleep apnoea, obstetric, head-and-neck oncological and post-radiotherapy, critically ill and emergency, and paediatric populations. We map this framework onto the 2025 European Society of Anaesthesiology and Intensive Care and Difficult Airway Society recommendations and outline the validation, calibration and equity requirements — including algorithmic bias across skin tone, sex and ethnicity — that must be met before precision airway assessment can enter routine practice.

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