人工智能和机器学习在心脏麻醉学中的概述和临床应用
Michael Mathis1, Kirsten R Steffner2, Harikesh Subramanian3
1Department of Anesthesiology, University of Michigan Medicine, Ann Arbor, MI.
Journal of cardiothoracic and vascular anesthesia
|March 7, 2024
概括
人工智能和机器学习通过分析电子健康数据,为改善心脏麻醉护理提供了新的途径. 本综述探讨了AI/ML应用,挑战和安全临床使用的伦理考虑.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 医疗信息学医学信息学
- 麻醉学 麻醉学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 越来越多地融入医疗保健.
- 电子健康数据的快速收集和解释在周边程序设置中为心脏麻醉学中AI/ML带来了独特的挑战和机会.
- 临床医生,管理人员和决策者需要了解AI/ML,以便安全有效地实施.
研究的目的:
- 审查最近在心脏麻醉学中的AI/ML应用.
- 通过具体的例子,探索AI/ML在医疗保健中的优势和局限性.
- 描述AI/ML在患者护理中的发展情况.
主要方法:
- 审查最近的三种AI/ML应用程序:麻醉监测深度,手术室资源优化和心声回声学.
- 介绍基本的AI/ML概念和方法.
- 讨论AI/ML实施的实际考虑和伦理问题.
主要成果:
- 人工智能/ML应用显示出增强心脏麻醉患者护理的潜力.
- 具体的例子强调了当前AI/ML技术在临床环境中的能力和局限性.
- 该评论为理解AI/ML在麻醉学中的整合提供了一个框架.
结论:
- 对于推进心脏麻醉,AI/ML具有显著的前景.
- 了解AI/ML概念,方法和伦理含义对于成功的临床采用至关重要.
- 对AI/ML的安全和持续实施的框架对于未来的医疗保健进步至关重要.
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