AI-based algorithms for difficult airway prediction show high sensitivity and specificity, significantly outperforming classical tests based on complex scales and indices.
Do AI-based models improve the prediction of difficult airways compared to traditional assessment?
AI-based models for difficult airway prediction demonstrate superior sensitivity and specificity compared to traditional clinical assessment tools.
Artificial Intelligence (AI) has become one of the most transformative technologies of the 21st century. It is poised to reshape medicine, as almost every field of hospital treatment has seen an increase in AI's presence. In this article, we focus on its impact in the field of anesthesia. We discuss its possible influence on difficult airway management, as it remains one of the most critical and potentially hazardous aspects of anesthesia, often leading to life-threatening complications. The accurate prediction of difficult airways can significantly improve patient safety. We covered the available literature on AI-based models for difficult airway prediction in comparison to traditional forms of airway assessment, as well as the predictive value of ultrasonography. We also address the narrative that AI-based algorithms show high sensitivity and specificity, with which they significantly outperform classical tests based on complex scales and indices.
Wilk et al. (Thu,) conducted a review in Difficult airway management. Artificial Intelligence (AI-based models) vs. Traditional forms of airway assessment was evaluated on Difficult airway prediction (sensitivity and specificity). AI-based algorithms for difficult airway prediction show high sensitivity and specificity, significantly outperforming classical tests based on complex scales and indices.