关于中风护理中生成性大语言模型的现状和未来方向:范围审查
XingCe Zhu1, Wei Dai1, Richard Evans2
1School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
JMIR medical informatics
|August 7, 2025
概括
生成型大语言模型 (gLLMs) 在中风治疗中表现有前途,但目前的应用还处于早期阶段. 需要进一步的研究,以确保安全有效的临床采用这些AI工具.
科学领域:
- 人工智能在医学中的应用
- 神经学 神经学
- 医疗信息学 医疗信息学
背景情况:
- 脑卒中对全球健康产生重大影响,导致残疾并压迫医疗保健资源.
- 生成型大语言模型 (gLLMs) 为中风护理挑战提供了潜在的解决方案.
- 需要对gLLM应用和中风护理中的表现进行全面审查.
研究的目的:
- 巩固关于在中风治疗中基于gLLM的干预措施的证据.
- 检查目前的景观,局限性和gLLM中风应用的未来方向.
- 确定这些干预措施的设计,报告和评估中的缺陷.
主要方法:
- 根据PRISMA-ScR和PCC框架进行范围审查.
- 在2024年12月搜索了6个主要的科学数据库,寻找在中风护理中的gLLM干预措施.
- 映射了包含的研究的关键特征和结果.
主要成果:
- 包括25项研究,其中追溯设计占主导地位 (64%).
- 主要的gLLM应用:临床决策支持 (40%),行政援助 (36%),患者互动 (20%).
- 确定了挑战:事实对齐,稳定性,可解释性,效率和临床采用.
结论:
- 在中风治疗中gLLM的应用是新生的,大多数是早期的实施.
- 在研究和临床翻译方面存在关键差距.
- 提出了一个可行的框架,用于开发在中风护理中具有影响力和可信度的AI工具.
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