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机器学习用于手术医生:新兴的临床应用.

Jacob Zeitlin1, Tristan B Weir2, Andrew J Miller2

  • 1Philadelphia Hand to Shoulder Center, Thomas Jefferson University Hospital, Philadelphia, PA; Weill Cornell Medicine, New York, NY.

The Journal of hand surgery
|May 5, 2025
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 正在通过改进诊断,预测结果和优化资源来彻底改变手术. 应对数据偏差和模型透明度等挑战是推动ML以改善患者护理和高效的实践管理的关键.

关键词:
人工智能的人工智能是人工智能.数据数据的数据数据的数据.机器学习是机器学习.结果预测结果预测.风险分层的风险分层.

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科学领域:

  • 医学 医学 医学 医学 医学
  • 计算机科学 计算机科学
  • 手术创新 在外科创新.

背景情况:

  • 机器学习 (ML) 为推进手术提供了巨大的潜力.
  • 机器学习的应用包括诊断,风险分层,结果预测和实践管理.
  • 目前的ML使用案例包括预测结果和优化外科手术安排.

研究的目的:

  • 探索ML在手术中的变革潜力.
  • 突出新兴的ML应用及其好处.
  • 解决在临床实践中实施ML的挑战和伦理考虑.

主要方法:

  • 在手术中新兴ML应用的审查.
  • 强调使用既定指导方针评估ML研究质量.
  • 讨论包括数据质量,偏差和模型解释性在内的挑战.

主要成果:

  • ML可以预测患者的结果 (例如,在手腕道释放后).
  • 机器学习算法可以优化外科手术安排,减少等待时间.
  • 关键的挑战包括数据偏见,缺乏概括性和道德问题.

结论:

  • 需要合作来创建多样化,高质量的数据集,用于手术中的ML.
  • 透明和可解释的ML算法对于临床医生的信任至关重要.
  • 将ML整合到临床工作流程中可以增强基于证据的个性化患者护理.