医疗保健聊天机器人中检索增强生成（RAG）增强型大语言模型的隐私挑战与解决方案：应用、风险及未来方向综述
Privacy Challenges and Solutions in Retrieval-Augmented Generation-Enhanced LLMs for Healthcare Chatbots: A Review of Applications, Risks, and Future Directions
AI总结：
本综述分析医疗保健中RAG增强型大语言模型的隐私挑战，综合相关研究，通过流水线框架剖析风险与保护机制，指出临床验证等关键差距并提出方向，为构建兼顾临床有效性与隐私保护的系统提供框架与路线图。
摘要：
检索增强生成（RAG）已迅速成为将大语言模型集成到临床和生物医学工作流中的变革性方法。然而，受保护健康信息（PHI）暴露等隐私风险的缓解仍不一致。本综述对RAG在医疗保健中的应用现状进行了全面分析，包括：（i）不同临床场景下的敏感数据类型；（ii）相关隐私风险；（iii）当前和新兴的数据隐私保护机制；（iv）患者数据隐私保护的未来方向。我们综合了23篇关于RAG在医疗保健中应用的文章，并通过涵盖数据存储、传输、检索和生成阶段的流水线结构框架系统分析隐私挑战，阐明潜在故障模式、其在威胁模型和系统机制中的根本原因及实际影响。基于此分析，我们批判性回顾了17篇关于RAG系统隐私保护策略的文章。评估发现关键差距：临床验证不足、缺乏标准化评估框架、缺少自动化评估工具。我们基于这些局限性提出可操作方向，并以行动呼吁作结。本综述为研究人员和从业者提供了理解医疗保健RAG隐私漏洞的结构化框架，为开发兼具临床有效性和强大隐私保护的系统提供了路线图。
英文摘要：
Retrieval-augmented generation (RAG) has rapidly emerged as a transformative approach for integrating large language models into clinical and biomedical workflows. However, privacy risks, such as protected health information (PHI) exposure, remain inconsistently mitigated. This review provides a thorough analysis of the current landscape of RAG applications in healthcare, including (i) sensitive data type across clinical scenarios, (ii) the associated privacy risks, (iii) current and emerging data-privacy protection mechanisms and (iv) future direction for patient data privacy protection. We synthesize 23 articles on RAG applications in healthcare and systematically analyze privacy challenges through a pipeline-structured framework encompassing data storage, transmission, retrieval and generation stages, delineating potential failure modes, their underlying causes in threat models and system mechanisms, and their practical implications. Building on this analysis, we critically review 17 articles on privacy-preserving strategies for RAG systems. Our evaluation reveals critical gaps, including insufficient clinical validation, absence of standardized evaluation frameworks, and lack of automated assessment tools. We propose actionable directions based on these limitations and conclude with a call to action. This review provides researchers and practitioners with a structured framework for understanding privacy vulnerabilities in healthcare RAG and offers a roadmap toward developing systems that achieve both clinical effectiveness and robust privacy preservation.
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