探索ChatGPT在紧急部门的ECG解释和结果预测方面的潜力
Arian Zaboli1, Francesco Brigo1, Marta Ziller2
1Innovation, Research and Teaching Service (SABES-ASDAA), Teaching Hospital of the Paracelsus Medical Private University (PMU), Bolzano, Italy.
The American journal of emergency medicine
|November 20, 2024
概括
在解读心电图 (ECG) 时,ChatGPT显示出中度的准确性,但其目前在识别关键病例方面的局限性限制了其在急诊室的使用. 进一步的AI进步可能会增强其作为医生支持工具的作用.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
背景情况:
- 心血管症状约占紧急病房访问的20%.
- 电心电图 (ECG) 对于诊断严重的心脏病状况至关重要.
- 紧急医生对心电图解释的准确性各不相同,这凸显了需要改进诊断工具的需要.
研究的目的:
- 评估ChatGPT与心脏病专家在解释心电图的协议.
- 评估ChatGPT在识别患有重大心脏不良事件 (MACE) 风险的患者方面的能力.
主要方法:
- 在梅拉诺医院急诊室进行了一项单一中心的回顾性观察研究.
- 将ChatGPT的ECG解释与心脏病学家的ECG解释进行了比较.
- 使用kappa统计数据量化了协议水平,并对MACE风险识别进行了二次分析.
主要成果:
- 在大多数心电图段上,ChatGPT与心脏病学家达成了很好的协议,在T波 (kappa=0.048) 和ST段 (kappa=0.267) 解释方面有显著的例外.
- 在关键病例评估中观察到显著的差异.
- 比起医生,ChatGPT发现了更多患有MACE风险的患者.
结论:
- 聊天GPT在心电图解释方面表现出中等准确性,但在评估关键病例方面存在局限性,这阻碍了其在紧急情况下的即时临床实用性.
- 进一步的研究和人工智能开发是必要的,以提高可靠性,并可能整合人工智能作为急诊医生的支持工具.
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