在心脏诊断中解释心电图像的GPT-4o模型的有效性:诊断准确性研究
Haya Engelstein1, Roni Ramon-Gonen2, Avi Sabbag3,4
1Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
JMIR AI
|August 22, 2025
概括
生成预训练变压器4 Omni (GPT-4o) 可以准确识别正常与异常心电图 (ECG),显示出作为人工智能分类工具的潜力. 然而,它通过心电图诊断特定心脏病的能力需要进一步发展.
科学领域:
- 医疗保健中的人工智能
- 医疗诊断
- 心脏病学
背景情况:
- 深度学习模型对心电图 (ECG) 分析具有前景,但复杂且昂贵.
- 大型语言模型 (LLM) 正在用于医学图像解释,但其ECG应用尚未得到充分探索.
- 多式人工智能GPT-4o提供了无需特定任务培训的可访问的电图分析替代方案.
研究的目的:
- 评估GPT-4o在解释12导心电图中的有效性.
- 评估GPT-4o对各种心电图的分类准确性.
- 探索提高GPT-4o在心电图分析中的性能的方法.
主要方法:
- 评估了6个常见的心电图诊断 (正常,心肌梗塞,心房动,束枝阻塞,节奏节奏) 使用80个未确定的心电图病例.
- 在零射击和少数射击学习条件下使用GPT-4o进行心电图像分析.
- 研究了三种情况:心电图像识别,二元正常/异常分类,并将多类分为6类.
主要成果:
- 在识别心电图像方面,GPT-4o实现了100%的准确性.
- 几次射击的学习提高了GPT-4o对正常和异常心电图分类的准确度,达到83% (95% CI 81. 8% - 84. 6%),增加了30%.
- 对特定病理的多类分类准确度有限,达到41%.
结论:
- GPT-4o有效地区分正常与异常的心电图,显示出作为可访问的人工智能辅助分类工具的潜力.
- 模型能够在没有专门培训的情况下解释心电图像,这表明它可以在各种环境中进行初步分析.
- 需要进一步的研究来提高GPT-4o在ECG诊断特定心脏病的准确性.
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