增强冠状动脉再血管化决策:大型语言模型作为多学科心脏团队决策支持工具的有希望作用
Karin Sudri1, Iris Motro-Feingold2, Roni Ramon-Gonen3,4
1ARC Innovation Center, Sagol Big Data and AI Hub (K.S., M.C.-S.), Sheba Medical Center, Tel Hashomer, Israel.
Circulation. Cardiovascular interventions
|November 6, 2024
概括
聊天GPT-4在预测冠状动脉再血管化的多学科心脏团队建议方面表现出高准确性,为复杂的冠状动脉疾病病例提供潜在的决策支持.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
背景情况:
- 临床指南建议多学科心脏团队 (MDHT) 讨论冠状动脉再血管化.
- 在MDHT实施的变化需要创新的决策支持工具.
- 像ChatGPT这样的语言学习模型 (LLM) 可能弥合了实施的差距.
研究的目的:
- 评估MDHT建议与LLM为冠状动脉再血管化产生的建议之间的一致性.
- 评估ChatGPT-3.5和ChatGPT-4在这个决策支持角色中的表现.
主要方法:
- 对86例冠状动脉血管学病例的分析,这些病例被转诊进行再血管化.
- 将MDHT决策与ChatGPT-3.5和ChatGPT-4的建议进行比较.
- 使用准确度,灵敏度,特异性和kappa统计数据对模型性能进行评估.
主要成果:
- 聊天GPT-4与MDHT决策取得了很高的一致性 (准确率为0.82,卡帕为0.59).
- 聊天GPT-3.5的一致性明显较低 (精度为0.67,卡帕为0.12).
- 详细的病例介绍提高了ChatGPT-4的准确性,特别是在左主疾病,3血管疾病和糖尿病患者中.
结论:
- 像ChatGPT-4这样的高级LLM显示出在冠状动脉疾病重血管化决策中作为支持性工具的潜力.
- 在特定的患者子组中,高精度表明AI在心脏病学中的定制应用.
- 法律法规可能有助于规范化和提高重血管化建议的一致性.
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