医疗保健中的大型语言模型对患者隐私的考虑:范围审查
Xiaoying Zhong1,2, Siyi Li1,2, Zhao Chen3
1Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, null, China.
Journal of medical Internet research
|November 21, 2025
概括
医疗保健中的大型语言模型 (LLM) 是有希望的,但缺乏患者健康信息 (PHI) 隐私保护. 未来的研究必须优先考虑强有力的隐私措施和透明度.
科学领域:
- 医疗保健人工智能的人工智能
- 医疗信息学 医疗信息学
- 数据 隐私 数据 隐私 数据
背景情况:
- 大型语言模型 (LLM) 在医疗保健中为患者护理和研究提供了巨大的潜力.
- 保护患者健康信息 (PHI) 是在医疗保健中应用LLM的一个关键挑战.
研究的目的:
- 评估目前在医疗保健研究中对患者隐私保护的方法,使用PHI和LLMs.
- 在医疗保健中开发和应用LLM时,确定在保护PHI方面需要改进的领域.
主要方法:
- 对2022年1月1日至2025年7月20日期间发表的文献进行了范围审查.
- 在PubMed和Embase上进行了搜索,重点是LLM开发和与PHI在医疗保健中的应用.
- 包括的研究集中在LLM建设,微调,优化,测试和使用PHI的性能比较.
主要成果:
- 在包括的464项研究中,9.7%忽视了伦理审查,31.9%缺乏患者知情同意.
- 38.4%的研究没有报告有效的PHI保护措施.
- 在匿名化和消除身份的方法中观察到显著缺乏透明度和细节.
结论:
- 提出了关于研究设计,实施和报告的全面建议,以提高PHI-LLM研究的隐私和透明度.
- 迫切需要更严格的监管框架和先进的隐私技术来保护PHI.
- 未来的医疗保健LLM应用程序必须平衡创新与强大的患者隐私保护,以实现道德和可信的进步.
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