当人工智能协助时,医生在图表审查期间更好地识别出血
Martin S Laursen1, Jannik S Pedersen1, Rasmus S Hansen2
1SDU Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.
Applied clinical informatics
|July 3, 2023
概括
人工智能 (AI) 显著提高了医生在临床病历审查期间检测出血事件的能力. 使用人工智能协助的医生发现了更多的事件,并对人工智能模型产生了积极的看法.
科学领域:
- 临床信息学 临床信息学
- 医疗人工智能 医疗人工智能
- 医疗信息学 医疗信息学
背景情况:
- 在电子健康记录 (EHR) 中检测出血对于患者护理至关重要.
- 手动查看图表是耗时的,容易忽略关键信息.
- 现有的出血识别方法缺乏足够的准确性和效率.
研究的目的:
- 评估人工智能 (AI) 模型对医生在图表审查期间识别出血事件的能力的影响.
- 评估医生对使用人工智能模型作为临床支持工具的看法.
- 分析医生在手动和人工智能辅助图表审查期间的阅读工作流程.
主要方法:
- 通过标记来自900个EHR的12个解剖位置的出血相关句子,开发了一个AI模型.
- 评估了人工智能模型在566例入院的测试队列上的表现 (灵敏度:93.7%,特异性:98.1%).
- 进行了一项临床使用研究,使用眼睛跟踪来分析阅读工作流程,并比较带有和没有人工智能辅助的图表审核性能.
主要成果:
- 医生在无助图表审查期间错过了超过33%的相关出血句子.
- 人工智能辅助的图表审查导致出血事件识别增加了48-49个百分点.
- 段落中的出血事件被忽视的频率高于指针的提及.
结论:
- 人工智能辅助的图表审查显著提高了医生识别出血事件的准确性.
- 医学医生普遍报告了对使用人工智能作为临床实践中支持性工具的积极看法.
- 人工智能模型表现出高性能,提高了诊断效率和潜在的患者结果.
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