人工智能用于预测困难的航空航线:一篇评论
Meruyert Alatau1, Johann Bauer2, Vitaliy Sazonov3,4
1Department of Medicine, School of Medicine, Nazarbayev University, Kerey Zhanibek Handar Street 5/1, Astana 010000, Kazakhstan.
Journal of clinical medicine
|December 11, 2025
概括
与传统方法相比,人工智能 (AI) 和机器学习 (ML) 模型在预测困难的呼吸道方面表现出更高的准确性. MixMatch深度学习模型实现了最高的性能,突出了AI.
科学领域:
- 麻醉学和急救医学 麻醉学和急救医学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 准确的难以预测呼吸道对于麻醉学和紧急医学的患者安全至关重要.
- 传统的评估工具往往缺乏足够的敏感性和特异性.
- 人工智能 (AI) 和机器学习 (ML) 为气道评估提供了有前途的进展.
研究的目的:
- 评估AI/ML模型对于难以预测通气道的性能.
- 将AI/ML模型与传统临床方法进行比较.
- 分析AI/ML模型的方法论稳定性,临床适用性和伦理考虑.
主要方法:
- 对2020-2025年的研究进行全面的文献搜索 (PubMed,科学网,Scopus).
- 包含AI/ML的原始研究和评论文章,以难以预测呼吸道.
- 提取和比较关键指标 (AUC,灵敏度,特异性) 和研究特征的定性分析.
主要成果:
- 人工智能模型显著超过了传统的评估工具.
- MixMatch半监督深度学习 (DL) 模型显示出最高的性能 (AUC 0.9435,灵敏度 89.58%,特异性 90.13%).
- 使用面部成像和DL的AI模型优于仅使用临床参数的AI模型,尽管注意到了方法异质性和有限的多样性.
结论:
- 人工智能和机器学习模型具有重大潜力,可以彻底改变困难的气道评估.
- 这些模型可以提高诊断准确性,并提供实时的临床决策支持.
- 解决方法标准化,人口多样性,可解释性和伦理问题对于临床实施至关重要.
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