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OphthoACR (眼科自动图表审查):一个人工智能驱动的工具,用于完全自动化眼科图表审查和队列数据分析

Karen M Chen¹, Kevin W Chen², Vlad Diaconita¹

¹Columbia University Irving Medical Center, Department of Ophthalmology, New York, New York, USA.

Translational vision science & technology

|October 9, 2025

概括

人工智能工具OphthoACR以94%的准确度自动化眼科图表审查,大大减少了处理时间. 这增强了临床研究,通过对大型患者队伍提供高效和可扩展的分析.

科学领域:

眼科医生眼科眼科
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

回顾性图表审查对于临床见解至关重要,但是劳动密集型和易出错的.
从非结构化的电子健康记录中提取数据在眼科中存在独特的挑战,因为依赖图像和文本.
现有的图表审查方法效率低下,阻碍了大规模的队列分析.

研究的目的:

引入OphthoACR,这是一个符合健康保险可移植性和问责法案的AI工具,用于眼科医学的自动化图表审查和队列分析.
评估OphthoACR在从患者记录中提取临床变量方面的准确性和效率.
将OphthoACR的性能与手动和人工智能辅助图表审查方法进行比较.

主要方法:

OphthoACR应用于91名患者的记录 (5834份文件),这些患者接受了二次眼内透镜手术.
该工具使用了一个微调的大型语言模型,集成到一个管道中,用于提取和语境化非结构化临床数据.
通过将OphthoACR的可变提取精度,特异性,灵敏度和处理时间与手动审查进行比较来评估性能.

主要成果:

在变量提取中,OphthoACR实现了94%的准确性,超过了手动审查 (83%).
该工具表现出高性能,具有97%的特异性,92%的灵敏度和0.70.0.的科恩 κ.
处理时间大大缩短:OphthoACR的每张图表80秒,手动审查的25.2分钟,提高了95%.

结论:

眼科ACR显著提高了眼科图表审查的准确性和效率.
这种由人工智能驱动的工具提供了一个自动化解决方案,用于分析大型患者队列,从而改变临床研究.
OphthoACR提供了一种高效,准确和可扩展的方法,用于回顾性图表审查.

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