人工智能生成的神经学咨询摘要提高了效率,减少了急诊部的文档负担
Alon Gorenshtein1,2,3, Shay Perek4,5, Yona Vaisbuch5,6
1AI in Neurology Laboratory, Ruth and Bruce Rapaport Faculty of Medicine, Technion Institute of Technology , 3525408, Haifa, Israel.
Scientific reports
|November 6, 2025
概括
大型语言模型 (LLM) 可以生成简洁的神经咨询报告,提高医生的效率. 虽然在语义上与人类报告相似,但LLM摘要可能缺乏个性化.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床文档 临床文档
背景情况:
- 医生在电子健康记录 (EHR) 上花费了过多的时间,影响了患者的护理.
- 准确的咨询报告对于护理连续性和临床决策至关重要.
- 神经学咨询报告是必不可少的,但有助于增加文档负担.
研究的目的:
- 用大型语言模型 (LLM) 评估自动生成的神经咨询报告的质量和实用性.
- 评估LLM在减轻神经科医生文档负担方面的潜力.
- 将LLM生成的报告与人类撰写的报告进行比较,以获得准确性,简洁性和风格.
主要方法:
- 利用250份急诊部 (ED) 神经咨询报告作为参考数据.
- 雇佣快速工程和检索增强生成 (RAG) 与LLM创建自动总结报告.
- 使用ROUGE分数和临床-BioBert语义相似性评估报告质量.
主要成果:
- 通过LLM生成的报告与神经科医生报告具有很高的语义相似性 (0.89).
- 法学士报告比较简洁 (61.56 vs. 94.75字) 和易于阅读 (FKGL 11.3 vs. 12.22).
- 在写作风格中发现了实质性的差异 (ROUGE-1 F1=0.25,ROUGE-2 F1=0.09,ROUGE-L F1=0.19).
结论:
- 通过LLM生成的神经报告提供语义准确性和简洁性.
- 与传统的神经科医生文档相比,自动化报告提供了一个更容易访问的格式.
- 虽然在内容传递方面有效,但LLM报告可能缺乏人类专家的细微写作风格.
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