# Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health

**Authors:** Bo Wen, Raquel Norel, Julia Liu, Thaddeus Stappenbeck, Farhana Zulkernine, Huamin Chen  
**arXiv:** [2406.13659](https://arxiv.org/abs/2406.13659)  
**Date:** 2024-06-19  
**Summary page:** https://www.summarizepaper.com/en/paper/2406.13659v1/

## Key points

- The paper explores the transformative potential of large language models (LLMs) in healthcare through conversational AI.
- LLMs are revolutionizing patient engagement by analyzing and generating conversations to enhance healthcare outcomes.
- Four case studies demonstrate the effectiveness of LLMs in extracting insights and summarizations from dialogues to engage patients in goal-oriented conversations.
- Analyzing mental health discussions on Reddit
- Developing personalized chatbots for cognitive engagement in seniors
- Summarizing medical conversation datasets
- Designing AI-powered patient engagement systems
- The paper highlights the applications of LLMs in handling unstructured conversational data in healthcare.
- Ethical considerations surrounding the use of LLMs in healthcare settings include data privacy, bias mitigation, transparency maintenance, and regulatory compliance.
- Close collaboration between AI experts and healthcare professionals is advocated to fully realize the potential of LLMs in digital health.
- Leveraging large language models for conversational analysis and generation offers innovative opportunities to enhance patient care through advanced AI technologies.

## Summary

The paper "Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health" by Bo Wen, Raquel Norel, Julia Liu, Thaddeus Stappenbeck, Farhana Zulkernine, and Huamin Chen explores the transformative potential of large language models (LLMs) in healthcare through conversational AI. The authors provide an in-depth analysis of how LLMs are revolutionizing patient engagement by analyzing and generating conversations to enhance healthcare outcomes. Through four compelling case studies, the authors demonstrate the effectiveness of LLMs in extracting insights and summarizations from dialogues to engage patients in goal-oriented conversations. These case studies include analyzing mental health discussions on Reddit, developing personalized chatbots for cognitive engagement in seniors, summarizing medical conversation datasets, and designing AI-powered patient engagement systems. The paper delves into the current landscape of LLMs in healthcare, highlighting their applications in handling unstructured conversational data. It also addresses important ethical considerations surrounding the use of LLMs in healthcare settings such as data privacy, bias mitigation, transparency maintenance and regulatory compliance. To fully realize the potential of LLMs in digital health, the authors advocate for close collaboration between AI experts and healthcare professionals. This collaborative approach is essential to address technical challenges and ensure the safety, efficacy and equity of these powerful tools in improving patient engagement and overall healthcare delivery. In conclusion,<Organization>, through leveraging large language models for conversational analysis and generation offers innovative opportunities to enhance patient care through advanced AI technologies.