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Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
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相关实验视频

Updated: Jan 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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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.

Eun Joung Kim1, JungYoon Kim2

  • 1Department of Game Contents, Kyungil University, Gyeongsan, Korea.

International neurourology journal
|December 8, 2025
PubMed
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.
关键词:
大型语言模型.医疗聊天机器人 医疗聊天机器人隐私-通过设计框架.泌尿器科 泌尿器科 泌尿器科 泌尿器科

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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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.