From algorithms to airways: Applying artificial intelligence to enhance airway assessment, management, and training
Artificial intelligence(AI) is advancing airway management applications, especially for airway assessment, clinical decision support, and training. Traditional assessment methods struggle with time and precision as complex airway disorders become more common. AI's powerful data processing and pattern recognition capabilities can assess patient imaging and clinical characteristics using deep learning algorithms to predict airway complications. In dynamic clinical environments, AI-assisted management solutions can improve airway control safety and efficiency by providing unique decision support. Additionally, AI systems using virtual reality and simulation training technologies can customize training programs for healthcare professionals based on airway difficulty, improving learning curves and clinical competencies in complex airway scenarios. AI in airway management shows its potential in assessment, clinical decision-making, and medical education. In clinical applications, we must also weigh AI's advantages and disadvantages. This review examines AI technology's current uses, future potential, and limitations in clinical practice and medical education. • AI uses deep learning to analyze imaging/clinical data to predict airway issues. • Clinical experience and AI tech optimize machine learning for real-world research. • Advantages and disadvantages of AI. • Optimal clinical-tech ratio is key to advancing future medical prediction.
Authors
- Bo Yang (ORCID: https://orcid.org/0000-0002-0419-1209)
- C. F. Qiao
- Yan Liu (ORCID: https://orcid.org/0009-0004-9212-0401)
- Jian Li (ORCID: https://orcid.org/0000-0001-9996-6021)
- Ming Guo
- Y. R. Hou (ORCID: https://orcid.org/0009-0004-2624-9979)
Institutions
- Soochow University (CN)
- Fourth Affiliated Hospital of China Medical University (CN)
- Second Affiliated Hospital of Soochow University (CN)
Publication Details
- Journal
- Trends in Anaesthesia and Critical Care
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1016/j.tacc.2025.101548
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65