将干预放射学报告分类为技术类别,使用微调的大型语言模型
Koichiro Yasaka1, Takuto Nomura2, Jun Kamohara2
1Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan. koyasaka@gmail.com.
Journal of imaging informatics in medicine
|December 13, 2024
概括
一个微调的大型语言模型准确地按技术分类干预放射学报告. 这种人工智能模型实现了与人类阅读器相比较的高精度,但运行速度明显更快.
科学领域:
- 医疗信息学 医疗信息学
- 放射学中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 干预放射学报告的分类对于数据分析和检索至关重要.
- 手动分类是耗时的,容易引起读者之间的变化.
- 自动化这个过程可以提高效率和一致性.
研究的目的:
- 开发和评估一个微调的大型语言模型 (LLM) 用于将干预放射学报告分类为技术类别.
- 为了比较LLM与人类读者 (放射科医生和放射科医生) 的表现.
主要方法:
- 对3198份干预性放射学报告进行了回顾性研究.
- 一个来自变压器的双向编码器表示 (BERT) 模型使用临床指示,成像诊断和图像查找部分进行了微调.
- 模型的分类准确性和宏观敏感性与手动分类技术类别进行了评估,并与三个人类阅读器进行了比较.
主要成果:
- 微调的LLM在独立测试数据集上实现了高精度 (0.988) 和宏敏度 (0.980).
- 该模型的表现与放射科医生和两名放射科住院医生的表现相当.
- 该LLM分类报告的速度明显更快,每名患者只需要0.178秒,比人类阅读器快17.5-19.9倍.
结论:
- 精心调整的LLM可以准确地将干预放射学报告分为技术类别.
- 开发的LLM证明了与专家人类读者一样的性能.
- 这种人工智能方法提供了相当大的速度优势,提高了放射学报告的效率.
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