利用大型语言模型促进患者参与：对话式人工智能在数字健康中的力量
Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health
AI总结：
本文概述LLMs在医疗保健中促进患者参与的应用，通过四个案例展示其处理非结构化对话数据的能力，讨论伦理考量与最佳实践，强调需跨社区合作实现其在数字健康中的安全有效应用。
摘要：
大型语言模型（LLMs）的快速发展为通过对话式人工智能转变医疗保健中的患者参与开辟了新机遇。本文概述了LLMs在医疗保健领域的当前格局，特别关注其在分析和生成对话以改善患者参与方面的应用。我们通过四个案例研究展示了LLMs处理非结构化对话数据的能力：（1）分析Reddit上的心理健康讨论；（2）开发针对老年人认知参与的个性化聊天机器人；（3）总结医疗对话数据集；（4）设计AI驱动的患者参与系统。这些案例研究表明，LLMs如何有效从非结构化对话中提取见解和摘要，并让患者参与有指导的、目标导向的对话。利用LLMs进行对话分析和生成，为许多以患者为中心的结局研究机会打开了新大门。然而，将LLMs整合到医疗保健中引发了关于数据隐私、偏见、透明度和法规遵从性的重要伦理考量。我们讨论了在医疗保健环境中负责任地开发和部署LLMs的最佳实践和指南。要充分发挥LLMs在数字健康中的潜力，需要AI和医疗保健专业人员社区密切合作，以解决技术挑战并确保这些强大工具的安全性、有效性和公平性。
英文摘要：
The rapid advancements in large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI. This paper presents an overview of the current landscape of LLMs in healthcare, specifically focusing on their applications in analyzing and generating conversations for improved patient engagement. We showcase the power of LLMs in handling unstructured conversational data through four case studies: (1) analyzing mental health discussions on Reddit, (2) developing a personalized chatbot for cognitive engagement in seniors, (3) summarizing medical conversation datasets, and (4) designing an AI-powered patient engagement system. These case studies demonstrate how LLMs can effectively extract insights and summarizations from unstructured dialogues and engage patients in guided, goal-oriented conversations. Leveraging LLMs for conversational analysis and generation opens new doors for many patient-centered outcomes research opportunities. However, integrating LLMs into healthcare raises important ethical considerations regarding data privacy, bias, transparency, and regulatory compliance. We discuss best practices and guidelines for the responsible development and deployment of LLMs in healthcare settings. Realizing the full potential of LLMs in digital health will require close collaboration between the AI and healthcare professionals communities to address technical challenges and ensure these powerful tools' safety, efficacy, and equity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
