评估人工智能结肠活检查工具所造成的错误的病理和临床影响
Harriet Evans1,2, Naveen Sivakumar3, Shivam Bhanderi4
1University of Warwick, Coventry, UK harriet.evans4@nhs.net.
BMJ open gastroenterology
|January 6, 2025
概括
用于结肠活检诊断的AI工具IGUANA在病例中具有7.9%的错误率,但大多数假负错误不会伤害患者. 临床背景是医疗保健中安全采用人工智能的关键.
科学领域:
- 组织病理学 组织病理学
- 人工智能的人工智能
- 数字病理学数字病理学
背景情况:
- 人工智能 (AI) 对改善医疗保健中的组织学诊断具有重大前景.
- 人工智能工具的采用受到缺乏对其临床背景的理解的阻碍.
- IGUANA是一种AI算法,旨在分类结肠活检并自动报告正常病例.
研究的目的:
- 为了回顾IGUANA AI算法在分类结肠活检时所犯的错误.
- 评估虚假阴性 (FN) 错误的病理和临床影响,优先考虑具有最大危害潜力的错误.
主要方法:
- 来自2080名患者的5054张整片图像 (WSIs) 的病理评估,重点关注FN错误.
- 评估WSI质量,准确诊断错过的实体,以及影响诊断错误的因素.
- 临床评估每个FN错误对患者护理的影响,包括错过的诊断,调查或治疗.
主要成果:
- 在164个案例中,共发生220个FN错误 (4.4%的WSIs,7.9%的案例).
- 错误范围从腺癌到轻度炎症;88.4%的FN错误对患者的护理没有影响.
- 减轻危害的因素包括低风险的息肉或在其他活检中检测出诊断特征;只有一个错误造成了重大伤害.
结论:
- 大多数来自IGUANA AI工具的FN错误不太可能对患者造成伤害.
- 尽管病例级错误率为7.9%,但当考虑临床背景时,人工智能工具可能适合采用.
- 在临床环境中评估AI错误对于在医疗保健环境中安全有效实施至关重要.
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