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Updated: Sep 8, 2025

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通过添加突出内容来改善大型语言模型的总结准确性:比较评估

Mahshad Koohi Habibi Dehkordi1, Yehoshua Perl1, Fadi P Deek2

  • 1Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, United States.

JMIR medical informatics
|July 24, 2025
PubMed
概括

在通过大型语言模型 (LLM) 总结之前,突出显示电子健康记录 (EHR) 注释中的详细信息显著提高了总结的准确性和完整性. 这种方法提高了患者出院摘要的可读性.

关键词:
在这里,我们可以看到AIAIAI.聊天GPT 聊天GPT 聊天聊天GPT总结 聊天GPT总结欧洲人权理事会 欧洲人权理事会电子健康报告摘要 电子健康报告摘要在法学士 (LLM) 课程中.在LLM的总结.摘要的准确性 摘要的准确性人工智能的人工智能是人工智能.临床笔记 摘要 总结放电说明是指一个关于放电的说明.放电说明 总结 总结电子健康记录 电子健康记录突出显示的 EHR 注释.大型语言模型

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