使用两个大型语言模型提取临床指南信息:评估研究
Hsing-Yu Hsu1,2, Lu-Wen Chen3, Wan-Tseng Hsu1
1Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan.
Journal of medical Internet research
|September 5, 2025
概括
两种先进的大型语言模型 (LLM) 有效地更新了决策支持系统的药物基因组学 (PGx) 临床指南,大大降低了手动审查的需求和成本.
科学领域:
- 药物基因组学 (PGx)
- 人工智能 (AI)
- 临床决策支持系统 (CDSS)
背景情况:
- 有效的个性化药物基因组学 (PGx) 需要将临床指南整合到决策支持系统中.
- 大型语言模型 (LLM) 提供了提取和更新PGx信息的自动化潜力.
- 手动审查PGx指南需要大量的时间和资源.
研究的目的:
- 通过使用两个先进的LLM来更新PGx临床指南,评估反复交叉比较和达成一致的策略的有效性.
- 评估GPT-4o和Gemini-1.5-Pro在提取和分类PGx指南中的性能.
- 确定LLM在简化将PGx指南整合到临床实践中的潜力.
主要方法:
- 两个LLM (GPT-4o,Gemini-1.5-Pro) 分类了385个PGx临床指南,每个模型进行了20次测试.
- 策略包括重复的交叉比较和一致性值 (预测<60%的一致性),以发现不一致性.
- 将LLM输出与专家注释的数据进行比较以评估准确性.
主要成果:
- 这两种LLM都实现了高的复制率 (GPT-4o:97.8%,Gemini-1.5-Pro:98.9%).
- 与专家标签相比,LLM的准确性很高 (GPT-4o: 93.5%,Gemini-1.5-Pro: 92.7%).
- 一致的预测减少了88.6%的手动审查需求,错误率最小 (0.3-0.5%) 和成本非常低 (US $ 0.76).
结论:
- 使用两个LLM为更新临床决策支持的PGx指南提供了一种成本效益和可扩展的方法.
- 通过LLM进行自动分类,大大降低了手动审查的负担,提高了临床适用性.
- 选择性手动审查对于确保准确性至关重要,但这种LLM驱动的方法优化了PGx指南的整合.
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