综合报告通过总结癌症病理学报告,使用大型语言模型进行综合报告
Sivaraman Rajaganapathy1, Shaika Chowdhury1, Xiaodi Li1
1Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
npj health systems
|February 2, 2026
概括
大型语言模型 (LLM) 可以自动化综合报告,提高准确性并减少医生负担. 这项研究表明,LLM在结构化病理报告方面取得了高准确性,从而提高了患者的护理.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在病理学中的应用
背景情况:
- 综合报告通过结构化的临床信息增强了患者的护理,但给医生带来了行政负担.
- 大型语言模型 (LLM) 的应用用于自动化综合报告是一个尚未探索的领域.
研究的目的:
- 探索最先进的LLMs在病理学中自动化综合报告的潜力.
- 开发和验证使用LLMs进行综合报告的全面自动化框架.
主要方法:
- 利用了7774份8种癌症类型的病理学报告的数据集,与医生注释的综合报告配对.
- 开发了一个自动化框架,将LLMs与参数有效优化,可扩展的提示模板和强大的评估策略集成在一起.
- 验证了内部和外部数据的框架,确保与病理学家的反应保持一致.
主要成果:
- 精心调整的LLAMA-2在所有数据元素中实现了BERT F1比分高于0.86.
- 在50%以上的数据元素中,LLAMA-2的F1得分超过0.94,超过22的11个数据元素.
- 手动评估的平均语义准确率达到77%,短期临床报告高达81%.
结论:
- 开发的LLM框架显示了在病理学中自动化综合报告的巨大潜力.
- 自动化综合报告可以提高效率和准确性,可能减轻医生的行政负担.
- 这种方法有望提高病理学报告的质量和完整性.
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