从放射学报告中提取信息的基于大型语言模型的方法的范围审查
Daniel Reichenpfader1,2, Henning Müller3,4, Kerstin Denecke5
1Institute for Patient-Centered Digital Health, Bern University of Applied Sciences, Biel/Bienne, Switzerland. daniel.reichenpfader@bfh.ch.
NPJ digital medicine
|August 24, 2024
概括
大型语言模型 (LLM) 显示出从放射学报告中提取信息的希望,但目前的研究主要集中在较旧的模型上. 需要进一步验证,以评估其现实世界的性能和通用性.
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:
- 放射学报告包含有价值的自由文本数据,但未得到充分利用.
- 自然语言处理 (NLP) 提供了一种从这些报告中获取结构化数据的方法.
研究的目的:
- 总结目前关于基于大型语言模型 (LLM) 从放射学报告中提取信息 (IE) 的研究.
- 确定该领域普遍存在的模型,挑战和未来方向.
主要方法:
- 根据PRISMA-ScR指南进行了范围审查.
- 在2023年8月1日,对五个数据库进行了搜索.
- 分析了34项符合纳入标准的研究.
主要成果:
- 该审查仅确定了基于预变压器和编码器的模型,没有提及先进的LLM.
- 外部验证表明,IE方法的整体性能下降.
- CT,MRI和胸部报告是最常见的主题.
- 关键的挑战包括缺乏外部数据验证和方法增强.
结论:
- 目前从放射学报告中对基于LLM的IE的研究是有限的,专注于较旧的架构.
- 法律法规可能会提高IE方法的通用性,但外部验证至关重要.
- 报告细节的差异阻碍了IE方法的可比性和透明度.
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