Summary of Leveraging Large Language Models For Patient Engagement: the Power Of Conversational Ai in Digital Health, by Bo Wen et al.
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, Huamin Chen
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper explores the applications of large language models (LLMs) in transforming patient engagement in healthcare through conversational AI. The study presents an overview of LLMs in healthcare, focusing on their capabilities in analyzing and generating conversations for improved patient engagement. Four case studies demonstrate the power of LLMs in handling unstructured conversational data, including mental health discussions on Reddit, a personalized chatbot for seniors, summarizing medical conversation datasets, and designing an AI-powered patient engagement system. The findings highlight the potential of LLMs in extracting insights and summarizations from unstructured dialogues and engaging patients in guided conversations. The paper also discusses ethical considerations regarding data privacy, bias, transparency, and regulatory compliance when integrating LLMs into healthcare settings. To realize the full potential of LLMs in digital health, close collaboration is needed between AI and healthcare professionals to address technical challenges and ensure safety, efficacy, and equity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how big language models can help make patient engagement in healthcare better through conversational AI. The study looks at the current state of large language models in healthcare and shows four examples of how they can be used to analyze and generate conversations that improve patient engagement. These examples include analyzing discussions on Reddit, creating a personalized chatbot for seniors, summarizing medical conversation datasets, and designing an AI-powered patient engagement system. The results show that big language models are good at extracting insights and summarizations from unstructured dialogues and engaging patients in guided conversations. The paper also talks about the importance of making sure data is private, not biased, transparent, and compliant with regulations when using these models in healthcare. |
