概括
手术中的人工智能 (AI) 在现实世界中应用有限. 大多数人工智能工具专注于术前任务,使用小数据集,缺乏外部验证,阻碍了广泛的临床采用.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 手术的决策是复杂的.
- 人工智能 (AI) 提供了潜在的支持.
- 在手术途径上实施人工智能的证据有限.
研究的目的:
- 在手术中映射人工智能支持的决策支持系统.
- 描述数据集,建模,验证和整合.
- 为了确定可扩展AI采用的差距.
主要方法:
- 145项研究的范围审查 (2018年1月至2025年5月).
- 在手术阶段和临床任务中分析AI系统.
- 数据集的表征,建模,验证和工作流集成.
主要成果:
- 文献集中在术前人工智能上;没有发现手术内研究.
- 大多数研究都使用了小型的,单一中心的,非公开的数据集与监督的CNN模型.
- 外部验证和工作流集成并不常见.
结论:
- 在AI的概括性,工作流的准备性和公平性方面存在重大差距.
- 优先事项包括多中心数据,严格的跨站点评估和临床上有意义的终点.
- 安全和可扩展的AI采用需要解决这些局限性.
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