大型语言模型开发基于证据的策略,用于初级和二级心血管预防
Giulia Lorenzoni1, Camilla Zanotto1, Anna Sordo1
1Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
European heart journal. Digital health
|September 23, 2025
概括
像ChatGPT-4o这样的大型语言模型提供了准确的心血管预防信息,但很难读取. 临床监督对于患者的理解和心血管疾病预防的最佳结果至关重要.
科学领域:
- 心脏病学 心脏病学
- 医疗保健中的人工智能
- 医学教育 医学教育
背景情况:
- 心血管疾病是全球死亡的主要原因之一.
- 有效的初级和二级预防策略对于管理缺血性心脏病和急性心肌梗塞至关重要.
- 互联网和人工智能工具越来越多地用于健康教育,补充了传统的医疗保健专业指导.
研究的目的:
- 评估大语言模型 (LLM) 产生的关于心血管疾病初级和二级预防的信息的准确性.
- 评估与心血管健康相关的LLM生成内容的可读性.
- 确定人工智能产生的健康信息对患者教育和临床实践的影响.
主要方法:
- 采用了观察性研究设计.
- 使用ChatGPT (4o版本) 来生成针对急性心肌梗塞风险预防的量身定制问题的答案.
- 专家心脏病学家使用利克尔特尺度评估了响应的准确性,并使用弗莱什阅读易度得分 (FRES) 测量了可读性.
主要成果:
- 在关于心血管疾病预防的评估项目中,ChatGPT-4o在75% (15分之20) 的问题上表现出准确性.
- 阅读度得分通常很低,这表明生成的内容很难让广大受众理解.
- 与更广泛的主题相比,心血管预防中的专业临床主题的准确性和可读性较低.
结论:
- 虽然ChatGPT-4o提供了关于心血管预防的准确信息,但其可读性对患者的理解构成了挑战.
- 临床监督仍然至关重要,以确保人工智能产生的健康信息的准确性和可理解性.
- 弥合准确性和可读性的差距对于在心血管疾病管理中实现最佳患者结果至关重要.
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