在泌尿病学中使用大型语言模型聊天机器人的隐私设计框架
1Department of Game Contents, Kyungil University, Gyeongsan, Korea.
International neurourology journal
|December 8, 2025
概括
本综述提出了一个隐私设计框架,用于在泌尿病学中安全使用大型语言模型 (LLM) 聊天机器人. 它确保数据保护和敏感健康信息的监管合规性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
背景情况:
- 大型语言模型 (LLM) 在医疗保健领域具有潜力,但由于敏感数据而引发隐私问题.
- 泌尿学数据,包括尿路,性和生殖健康信息,是特别敏感的.
- 现有的框架可能无法充分解决泌尿病学LLM的独特隐私和治理需求.
研究的目的:
- 为安全的临床部署在泌尿病学LLM聊天机器人的隐私-by-design技术和治理框架.
- 确保敏感泌尿病数据的保护,同时实现可靠的临床应用.
- 建立安全,治理和对医学LLM采用问责制的标准.
主要方法:
- 集成敏感数据的现场算法去识别.
- 实施联合学习,具有差异隐私和安全聚合.
- 使用安全检索增强生成与源引用和审计日志.
主要成果:
- 建立了一个联合,可解释和可审计的管道.
- 保护数据主权,增强患者的信任和数据安全.
- 改善了临床可靠性和监管合规性.
结论:
- 拟议的框架使LLM聊天机器人在泌尿病学中安全和负责任地部署.
- 泌尿病学是验证LLM安全和治理标准的关键测试案例.
- 这种方法有助于在各种临床领域更广泛地采用基于LLM的医疗聊天机器人.
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