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.

通过四个案例研究展示了大型语言模型在医疗保健中的应用，包括分析 Reddit 上的心理健康讨论、为老年人进行认知参与开发个性化聊天机器人、总结医疗对话数据集和设计 AI 助力病患参与系统。同时讨论了在医疗保健环境中引入大型语言模型的道德考虑、最佳实践和指南。