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用大型语言模型生成口语放射学报告

Cynthia Crystal Tang¹, Supriya Nagesh², David A Fussell¹

¹Department of Radiological Sciences, University of California, Irvine, Irvine, CA 92868, United States.

Journal of the American Medical Informatics Association : JAMIA

|August 23, 2024

概括

放射科医生现在可以使用一种新的大型语言模型 (LLM) 方法生成患者友好的放射学报告. 这种方法提高了报告的可读性和准确性,有利于患者的理解,而不会增加放射科医生的工作量.

科学领域:

医疗信息学医疗信息学
自然语言处理自然语言处理.
放射学放射学是一门学科.

背景情况:

患者对医疗记录的访问正在增加.
放射学报告包含复杂的医学术语,阻碍了患者的理解.
手动创建患者友好的报告对放射科医生来说是耗时的.

研究的目的:

开发一种自动化方法,将放射学报告翻译成可访问的,面向患者的语言.
利用大型语言模型 (LLM) 提供专业提示,用于口语报告生成.
评估LLM生成的患者友好的报告的准确性和可读性.

主要方法:

开发了一种新的方法,使用具有专业提示的LLM.
医学术语在LLM提示符中自动提取和定义.
100份未经识别的神经放射学报告被翻译成四个阅读级别.
放射科医生对翻译进行了评估,以确定其准确性,可喜可爱性,潜在危害性和可读性.

主要成果:

在八年级的阅读水平上,LLM方法在发现部分达到88%的准确性,在印象部分达到93%的准确性.
在所有年级,翻译比纯粹的基线方法准确20%以上.
标准可读性指数表明与原始报告相比,可读性有所改善.

结论:

在八年级水平的LLM生成的翻译提供了准确性和可读性的最佳平衡.
这种方法与面向患者的材料的国家卫生传播建议保持一致.
自动翻译可以提高患者对放射学报告的理解,而不会给放射科医生带来负担.

关键词:

大型语言模型机器学习是机器学习.自然语言处理自然语言处理.快速的工程迅速的工程放射学放射学是指放射学

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