医疗保健中的大型语言模型架构:研究视角的范围审查
Florian Leiser1, Richard Guse1, Ali Sunyaev2
1Research Group Critical Information Infrastructures, Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Journal of medical Internet research
|June 19, 2025
概括
大型语言模型 (LLM) 提供临床支持,但架构选择很重要. 基于生成预训练变压器 (GPT) 的模型在通信方面表现出色,而基于变压器 (BERT) 的双向编码器表示模型更适合用于知识发现等医疗任务.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 显示了帮助医疗保健专业人员完成报告撰写和诊断沟通等任务的潜力.
- 目前的研究探讨了医学实践中的LLM应用,但往往忽视了选择适当的LLM架构的关键方面.
- 了解不同LLM架构之间的细微差别对于在临床工作流程中有效实施至关重要.
研究的目的:
- 审查和分析在医疗保健研究中使用的LLM架构 (基于BERT和基于GPT).
- 确定各种LLM架构家族对医学领域内不同研究目标的适用性和好处.
- 提供对LLM架构在医疗保健环境中的差异应用的见解.
主要方法:
- 进行了范围审查,以确定在医疗保健研究中使用的LLM.
- 手稿来源于著名的数据库,包括PubMed,arXiv和medRxiv.
- 开放和选择性编码分析了11个维度的114个手稿,重点关注使用,技术方面和研究重点.
主要成果:
- 确定了四个主要研究重点,其中LLM绩效是最突出的.
- 基于生成预训练变压器 (GPT) 的模型主要用于沟通任务,包括考试准备和患者互动.
- 基于变压器的双向编码器表示 (BERT) 模型经常应用于医学任务,例如知识发现和模型增强.
结论:
- 基于GPT的模型更适合于以通信为中心的应用程序,如报告生成和患者交互.
- 基于BERT的模型在医疗文本分类和知识发现等创新应用中显示出卓越的性能.
- 架构上的差异,包括单向 (GPT) 与双向 (BERT) 处理,以及BERT对域特定扩展的适应性,有助于它们在医疗保健应用中的独特优势.
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