多模式大语言模型与专用心电图AI的比较诊断性能,用于从心电图图像检测心肌梗塞:比较研究
Haemin Lee1,2, Sooyoung Yoo3, Joonghee Kim1,2
1Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea.
JMIR AI
|September 17, 2025
概括
与专门的AI工具相比,一般的大型语言模型,如ChatGPT和Gemini在ECG诊断心肌梗塞 (MI) 的表现不佳. 特定领域的人工智能对于准确的临床解释至关重要.
科学领域:
- 心脏病学和人工智能的人工智能
- 医学成像分析 医学成像分析
- 医疗保健中的机器学习
背景情况:
- 准确的心电图 (ECG) 解释对于紧急心肌梗塞 (MI) 诊断至关重要.
- 多模式大语言模型 (LLM) 显示出临床解释的潜力,但它们依赖波形数据与文本线索的依赖性尚不清楚.
- 需要直接比较一般的LLM和专门的ECGAI工具.
研究的目的:
- 评估ChatGPT和双子座的诊断性能,以从心电图像中检测心脏病发作.
- 为了比较这些一般的LLM与专门的AI工具ECG Buddy的性能.
主要方法:
- 来自巴基斯坦的公开可用的12个心电图数据集 (239个MI阳性,689个MI阴性病例) 的追溯分析.
- 聊天GPT (GPT-4o) 和双子座 (2.5 Pro) 被查询MI的信心.
- ECG Buddy分析了对ST升高MI,急性冠状动脉综合征和心肌损伤生物标志物的ECG图像.
主要成果:
- 电脑心电图Buddy获得了高精度 (96.98%),AUC (98.8%),灵敏度 (96.65%) 和特异性 (97.10%).
- 聊天GPT表现较差:准确率为65.95%,AUC为57.34%,灵敏度为36.40%,特异性为76.2%.
- 双子 2.5 Pro表现不佳:准确率为29.63%,AUC为51.63%,灵敏度为97.07%,特异性为6.24%.
- 对诊断的LLM解释往往是不准确的;ECG Buddy显著优于两种LLM (P<.001).
结论:
- 像ChatGPT和Gemini这样的一般LLM与基于ECG的MI诊断的ECGBuddy等专业AI工具相比表现明显差.
- 特定领域的AI模型对于在临床环境中实现可靠和强大的诊断准确性至关重要.
- 虽然LLM可能会随着进一步的培训而得到改善,但专门的AI目前仍然对ECG准确检测MI至关重要.
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