通过保护隐私的语言模型保护敏感的医疗数据
IEEE journal of biomedical and health informatics
|August 27, 2025
概括
医疗保健中的大型语言模型 (LLM) 是一个新的隐私保护框架. 它确保准确的医疗建议,同时保护敏感的患者数据,增强对人工智能驱动的健康咨询的信任.
科学领域:
- 人工智能
- 数字健康
- 医疗信息学
背景情况:
- 大型语言模型 (LLM) 提供了改善医疗服务的潜力,但由于敏感数据的处理而引发了重大隐私问题.
- 目前在医疗保健领域的LLM应用可能会导致未经授权的数据暴露和法规不合规.
- 人工智能在医疗领域的广泛应用受到强有力的患者数据保护的阻碍.
研究的目的:
- 推出一个新的基于LLM的医疗咨询隐私保护框架.
- 应对人工智能驱动的医疗保健系统中保护敏感健康信息的关键挑战.
- 通过LLM提供安全可靠的医疗咨询,而不影响用户的隐私.
主要方法:
- MediGuard使用适应性信息模糊来保护敏感的医疗数据.
- 该框架包括安全访问协议和数据治理的审计机制.
- 它只处理非敏感信息,同时保持语义完整性,以便准确的医学推断.
主要成果:
- 与现有的方法相比,MediGuard在广泛的测试中显示出更高的隐私保护.
- 该框架在医疗问答任务中实现了高临床准确性,即使在严格的隐私限制下.
- 跨多个数据集的性能评估证实了MediGuard在平衡隐私和实用性的有效性.
结论:
- MediGuard提供安全,可信和临床可靠的AI医疗咨询解决方案.
- 该框架为医疗保健领域的隐私意识的人工智能建立了新的基准.
- 实施MediGuard可以促进LLM安全整合到数字医疗生态系统中.
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