概括
大型语言模型 (LLM) 在神经学中表现有前途,但目前的证据受到偏见和缺乏前性验证的限制. 未来的临床使用需要先进的架构,临床医生监督,以确保安全有效的实施.
科学领域:
- 临床信息学 临床信息学
- 人工智能在医学中的应用
- 神经学 神经学
背景情况:
- 大型语言模型 (LLM) 正在探索临床神经学应用.
- 关于它们在现实世界中的实用性,安全性和最佳实施,存在不确定性.
- 为了解决这些知识差距,进行了系统审查.
研究的目的:
- 系统地审查和描述临床神经病学当前的LLM应用.
- 评估在神经病学中使用LLM证据的质量.
- 识别知识差距和未来的方向,在神经病学LLM实施.
主要方法:
- 在多个数据库 (PubMed,Embase,Scopus,Web of Science,CENTRAL) 中按照PRISMA指南进行系统的文献搜索,从2022年1月到2026年2月.
- 包括同行评审的研究评估临床相关的神经病学任务的LLMs使用文本或多式联络输入.
- 数据提取和偏差风险评估使用QUADS-AI由两个独立的审稿人;证据的叙事综合.
主要成果:
- 包括8个神经学子专业的36项研究 (2023-2026年);大多数是模拟或回顾性分析.
- 在诊断分类 (AUC 0.75-0.94) 和信息提取 (F1 0.89-0.90) 等受限制的任务中,LLM表现出很高的性能.
- 安全问题包括幻觉,过度自信的建议和所有研究中偏差的高风险;在开放式任务中精度较低.
结论:
- LLM显示了特定神经学工作流程的潜力,但目前的证据是初步的,并受到异质性和偏见的限制.
- 临床翻译需要检索增强生成 (RAG) 和代理架构来进行多步骤的任务规划,验证和可审计的输出.
- 临床监督和前性验证对于LLMs在临床神经病学中安全有效地整合至关重要.
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