隐藏和寻找:保护隐私的人工智能与罕见疾病诊断的可行性研究
medRxiv : the preprint server for health sciences
|February 6, 2026
概括
同型加密 (HE) 通过允许对加密的患者数据进行计算,使得安全的AI驱动的临床诊断成为可能. 这种保护隐私的框架将HE与大型语言模型 (LLM) 集成在一起,以便在不影响敏感信息的情况下准确诊断罕见疾病.
科学领域:
- 医疗保健中的人工智能
- 密码学 密码学 密码学 密码学
- 医疗信息学 医疗信息学
背景情况:
- 临床决策支持系统通常需要共享敏感的患者数据,这可能会给隐私带来风险.
- 同型加密 (HE) 允许对加密数据进行计算,保护患者的机密性.
研究的目的:
- 开发一个大型语言模型 (LLM) 辅助诊断框架,使用HE进行私人临床文本分析.
- 验证HE在罕见疾病 (RD) 诊断中的实用性,并确认它不会阻碍系统性能.
主要方法:
- LLM将患者病史和研发知识库嵌入向量中,然后使用HE进行加密.
- 通过在安全空间中比较加密载体,生成诊断建议.
- 该框架使用50个合成罕见疾病病例报告进行了评估.
主要成果:
- 他有效地保护私人信息免受反向嵌入攻击.
- HE对诊断准确性的影响很小 (nDCG 0.6108与0.6083).
- 准确性和性能在五个LLM中是一致的,可以调整.
结论:
- 保护隐私的框架促进了跨机构的安全AI解决方案,消除了对本地数据去识别的需求.
- 将高等教育与LLM检索相结合,可以促进安全,准确和广泛可访问的AI服务.
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