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手术研究中的人工智能:成就和未来方向
Michael P Rogers1, Haroon M Janjua1, Steven Walczak2
1Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
American journal of surgery
|November 19, 2023
概括
这项研究回顾了手术中的机器学习 (ML),强调了传统方法和使用大数据的先进方法的需求. 了解ML的理解ML的理解
科学领域:
- 外科信息学外科信息学
- 人工智能在医学中的应用
- 计算生物学 计算生物学
背景情况:
- 电子健康记录和计算能力的扩散促进了医疗保健中的AI和机器学习 (ML).
- 目前正在为所有护理阶段 (手术前,手术内,术后) 的手术患者开发算法方法.
- 外科医生需要批判性地评估这些ML方法的优缺点.
研究的目的:
- 调查目前在外科实践中对机器学习应用的理解.
- 探索手术中ML的未来方向,包括风险分层,临床数据分析和决策支持.
- 强调了解ML的重要性,以便在手术中适当实施.
主要方法:
- 对现有的人工智能 (AI) 文献进行了审查.
- 强调了与外科领域相关的当代ML方法.
- 审查的重点是适用于外科实践的方法.
主要成果:
- 引入了传统的机器学习 (ML) 方法及其外科实施.
- 该研究强调,由于大数据的兴起,需要超越传统的ML方法.
- 该审查涵盖了与外科应用相关的各种ML技术.
结论:
- 人工智能 (AI) 对临床外科及其子专业的潜在影响是巨大的.
- 随着人工智能在外科文献和实践中变得越来越普遍,了解其机制和局限性至关重要.
- 在外科手术中对人工智能的明智实施需要对其能力和缺点有彻底的了解.
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