评估生物医学事实检查的语言模型:癌症变异解释验证的基准数据集
Caralyn Reisle1,2, Cameron J Grisdale1, Kilannin Krysiak3,4
1Canada's Michael Smith Genome Sciences Centre.
bioRxiv : the preprint server for biology
|September 26, 2025
概括
自动化事实核查系统可以验证癌症变体索赔,加速精确瘤学. 在CIViC-Fact上微调的AI模型实现了89%的准确性,提高了知识库策划效率.
科学领域:
- 基因组医学是一种基因组医学.
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 精确的基因组变异解释对于精确瘤学至关重要,但由于缓慢的,依赖专业知识的手动审查过程而受到阻碍.
- 像CIViC这样的公共知识库策划了变体解释,但在验证和审查方面遇到了瓶.
研究的目的:
- 开发一个基准数据集和自动化管道 (CIViC-Fact),用于验证癌症变异声明的测试系统.
- 评估人工智能模型在生物医学事实检查中的效率和准确性,用于知识库策划.
主要方法:
- 创建了CIViC-Fact,将结构化的声明与来自全文文章的句子级证据联系起来,包括专家注释.
- 评估了专有和开源语言模型,对CIViC-Fact.上的一种开源模型进行了微调.
- 将事实检查管道应用于真实的CIViC条目,以评估AI辅助分拣的有效性.
主要成果:
- 一个微调的开源语言模型在验证癌症变异声称方面取得了最高的准确性 (89%).
- 事实核查管道发现,审查不到20%的内容,专注于标记的条目,可以检测到一半以上的错误.
- 人工智能辅助的分拣显著加快了审查过程,同时保持了专家的监督.
结论:
- CIViC-Fact为生物医学事实核查和改进知识库策划提供了一个强大的框架.
- 人工智能辅助审查可以通过将专家注意力集中在关键条目上来提高策展人的效率.
- 这种方法支持在精密瘤学中更严格,更有效的知识库策划.
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