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在临床试验中使用人工智能来判断主要的不良心血管事件

Pablo M Marti-Castellote1, Samarra Badrouchi2, Brian Claggett2

  • 1Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

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|January 6, 2026
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概括

人工智能 (AI) 有效地裁决主要不良心血管事件 (MACE),与医生委员会达成高度一致. 这种人工智能系统可以简化临床试验中的MACE评估,降低成本并提高可重现性.

关键词:
人工智能的人工智能是人工智能.临床试验是指临床试验中的临床试验.结果的裁决结果.

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科学领域:

  • 心血管研究的心血管研究.
  • 医疗人工智能的人工智能
  • 临床试验方法论 临床试验方法论

背景情况:

  • 主要不良心血管事件 (MACE),包括心血管死亡,心肌梗塞 (MI) 和中风,是关键的临床结果.
  • 医生领导的临床事件委员会 (CEC) 在全球随机试验中对MACE的裁决是准确的,但劳动密集型.
  • 人工智能 (AI) 为成本效益高,可重复的MACE裁决提供了一个潜在的解决方案.

研究的目的:

  • 开发和验证基于人工智能的MACE裁决系统.
  • 在一项大型全球临床试验中,将人工智能系统的性能与传统的CEC判断进行比较.

主要方法:

  • 一个AI系统"Auto-MACE"是使用OpenAI的o1-mini语言模型开发的,用于MACE裁决.
  • 采用临床长型模型,将AI裁决赋予信心级别.
  • 在涉及5,661名患者的PARADISE-MI试验中,Auto-MACE与CEC的裁决得到了验证.

主要成果:

  • 自动MACE实现了69%的死亡,46%的MI和81%的中风的自信判断.
  • 自动MACE和CEC之间的一致性在自信判断方面很高 (97%的死亡,89%的中风,88%的中风).
  • 所有事件的总体一致性为死亡率为86%,心脏病发作率为76%,中风率为84%,治疗效果估计类似.

结论:

  • 基于人工智能的MACE裁决显示出与CEC裁决的实质性一致,特别是在心血管死亡和中风方面.
  • 人工智能系统的信心水平对于高精度至关重要.
  • 整合人工智能进行初步裁决,在不确定的情况下进行CEC审查,可以优化工作量并保持临床试验的准确性.