概括
大型语言模型 (LLM) 在传染病诊断方面表现有前途,但存在重大安全问题,包括错误和伪造的内容,需要专家监督临床使用.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 传染病管理 传染病管理
背景情况:
- 传染病的临床推理需要经过验证的证据.
- 大型语言模型 (LLM) 在医疗保健中越来越多地用于诊断和抗菌药物管理.
- 在传染病决策中LLM的安全性和可靠性仍然未确立.
研究的目的:
- 系统地审查评估传染病决策的LLM的研究.
- 评估该领域各种LLM系统的性能,安全性和可靠性.
主要方法:
- 使用GPT,Claude,Gemini和检索增强系统进行的研究的系统性审查.
- 在PubMed,CENTRAL,Scopus和Web of Science进行的搜索 (2018年1月至2025年9月).
- 使用QUADAS-AI评估的偏差风险.
主要成果:
- 包括31项研究;大多数是横截面和基于图片的.
- 90%的研究报告了安全问题,包括不完整的答案和伪造的内容.
- 对结构性感染来说,LLM实现了80-100%的诊断灵敏度,但在抗菌药物管理方面达成约50%的协议.
结论:
- 在特定的诊断任务中,LLM显示出潜力,但目前在独立的临床应用中不可靠.
- 高错误率和不一致的推理需要在部署之前进行专家监督和验证.
- 与标准的LLM相比,检索增强系统显示出更好的特异性和更少的幻觉.
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