批判性评估人工智能和深度学习工具用于手术内神经外科手术:作与证据对比
Tirath Patel1, Ehtisham Haider2, Amir Riaz2
1Department of Neurosurgery, Trinity Medical Sciences University School of Medicine, Kingstown, Saint Vincent and the Grenadines.
Annals of medicine and surgery (2012)
|February 12, 2026
概括
人工智能 (AI) 在外科手术方面表现有前途,但面临着挑战. 严格的测试和多中心研究是必要的,以确保安全的临床采用,并确保人工智能辅助,而不是取代,外科医生的判断.
科学领域:
- 手术技术 手术技术
- 医疗人工智能 医疗人工智能
- 在手术内成像.
背景情况:
- 人工智能 (AI) 越来越多地被整合到手术工作流程中,协助导航,仪器跟踪和图像分析等任务.
- 当前的人工智能应用显示出技术上的希望,但受限于小型,非多样化的数据集,导致过度匹配和糟糕的概括性.
- 外部验证和临床影响的评估 (例如,对并发症,切除程度) 很少.
研究的目的:
- 评估人工智能在手术内环境中的当前状态和未来方向.
- 识别内科人工智能工具临床翻译的障碍.
- 概述了在外科手术中负责开发和采用AI的要求.
主要方法:
- 在手术内工作流程中审查当前的AI应用.
- 分析局限性,包括数据异质性,缺乏外部验证和不一致的报告标准.
- 检查与伦理,监管和资源相关的障碍.
主要成果:
- 人工智能在导航,仪器跟踪,超声分析,视频细分和MRI重建方面展示了潜力.
- 存在重大挑战,包括小型数据集,过度拟合,有限的外部验证和缺乏以结果为重点的试验.
- 监管指南 (FDA,欧盟,世卫组织) 现在要求生命周期监测和现实世界的证据.
结论:
- 将手术内人工智能转化为临床实践需要处理数据标准化,报告和多中心验证.
- 共享数据库和标准化报告对进步至关重要.
- 虽然人工智能不会取代外科医生,但严格的开发和测试可以释放其临床价值.
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