对严重大动脉狭窄症的AI自动化风险操作分层:一项概念验证研究
Dorian Garin1,2, Diego Arroyo1, Ioannis Skalidis1
1Department of Cardiology, University and Hospital Fribourg, 1708 Fribourg, Switzerland.
Journal of clinical medicine
|December 11, 2025
概括
大型语言模型 (LLM) 可以准确计算EuroSCORE II,并与严重大动脉狭窄风险的心脏团队决定相匹配. 然而,幻觉和缺乏结果验证限制了目前的临床使用.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 准确的手术风险分层对于严重的大动脉狭窄症的治疗选择至关重要.
- 多学科心脏小组为这些患者提供必要的风险评估.
- 使用大型语言模型 (LLM) 的自动化工作流程被探索,以复制心脏团队的评估.
研究的目的:
- 开发和评估基于LLM的自动化工作流程,用于复制心脏团队在严重大动脉狭窄的风险分层.
- 为了比较基于LLM的风险计算与传统方法和心脏团队决定的准确性.
主要方法:
- 对231名严重大动脉狭窄患者进行了回顾性分析.
- 开发了一个自动化的GPT-4o系统用于数据提取,EuroSCORE II计算 (算法与LLM) 和风险分层.
- 通过将LLM方法 (有/没有EuroSCORE II) 与心脏团队决定进行比较,评估了风险分层的准确性.
主要成果:
- 在LLM工作流中,患者的处理速度很快 (32.6±6.4秒).
- 与算法计算相比,LLM计算的EuroSCORE II与心脏团队值的平均差异较小.
- 没有EuroSCORE II的指导方针集成的LLM方法在风险分层方面获得了最高的准确性 (90.0%) 和AUC (0.93).
- 当数据缺失时,观察到心血管风险因素的系统幻觉.
结论:
- LLM在EuroSCORE II计算中表现出准确性,并与心脏团队的决定达成高一致性 (90%).
- 尽管准确性充满希望,但LLM幻觉,可重现性问题和缺乏临床结果验证都阻止了立即的临床应用.
- 需要进一步的研究来解决LLM在重症大动脉狭窄管理中临床整合之前的局限性.
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