人工智能与人类专业知识对比:在心脏骤停后基于心电图检测闭塞性心肌梗塞
Claudio Silwanis1, Johannes Eder1, Alexander Fellner1
1Kepler University Hospital, Department of Cardiology and Medical Intensive Care, Medical Faculty, Johannes Kepler University, Linz, Austria.
Resuscitation
|November 21, 2025
概括
一个深度神经网络 (心灵女王) 和人类专家在心脏骤停后显示了精确的心电图解读. 一般的大型语言模型显示诊断性能差,突出显示它们不适合这项关键任务.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 心脏骤停 (CA) 后精确的心电图 (ECG) 解释对于识别闭塞性心肌梗塞 (OMI) 至关重要.
- 复苏后的文物使心电图分析复杂化,人工智能 (AI) 在这种情况下的诊断性能不确定.
研究的目的:
- 评估不同人工智能工具和人类专家在心脏骤停后解释心电图的诊断准确性.
- 为了比较基于深度神经网络 (Queen of Hearts - QoH) 和大型语言模型 (LLM) 的AI聊天机器人与人类专家的性能,用于检测急性冠状动脉封闭 (ACO) 和OMI.
主要方法:
- 一项单中心研究涉及97名心脏骤停 (CA) 后复苏的成年患者.
- 自发循环 (ROSC) 后回归心电图由人类专家 (HE),QoH,ChatGPT和EKG分析师进行分析.
- 主要结局是OMI和ACO的接收器操作特征曲线 (AUROC) 下的区域,通过冠状动脉血管学证实.
主要成果:
- 在ACO (0.846) 和OMI (0.745) 中,QoH获得了最高的AUROC,超过了HE (ACO:0.735;OMI:0.635).
- 基于LLM的AI聊天机器人 (ChatGPT,EKG分析师) 在ACO和OMI方面显示出明显较低的AUROC.
- 人工智能聊天机器人表现出高灵敏度,但对ACO和OMI的特异性较低,而QoH和HE表现出更平衡的性能.
结论:
- 在QoH深度神经网络和人类专家展示了强大的诊断准确性在心脏骤停后心电图解释.
- 由于过度诊断和特异性差,一般的LLM不适合ECG解释.
- 像QoH这样的特定领域的人工智能工具可能在重症监护环境中提供有价值的补充支持.
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