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Using Large Language Models for Chronic Disease Management Tasks: Scoping Review.

Henry Mukalazi Serugunda¹, Ouyang Jianquan², Hasifah Kasujja Namatovu³

¹Department of Information Technology, School of Computing and Informatics Technology, Makerere University, Kampala, Uganda.

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Summary

This summary is machine-generated.

Large language models (LLMs) show promise for chronic disease management, aiding patients and practitioners. Addressing challenges like accuracy and ethics is crucial for safe and equitable integration into healthcare.

Keywords:

GPT NLP artificial intelligence in health care chronic diseases disease management generative pre-trained transformer large language models natural language processing

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Area of Science:

Digital Health
Artificial Intelligence in Healthcare
Chronic Disease Management

Background:

Chronic diseases pose significant healthcare challenges, necessitating effective management strategies.
Digital technologies, including wearables and apps, are common, but large language models (LLMs) offer new potential.
The application and challenges of LLMs in chronic disease management require further exploration.

Purpose of the Study:

To conduct a scoping review of large language model (LLM) applications in chronic disease management.
To identify current uses, challenges, and propose recommendations for LLM integration.
To synthesize findings from recent primary studies on LLMs in this domain.

Main Methods:

Systematic search of PubMed, IEEE Xplore, Scopus, and Google Scholar (Jan 2023 - Jan 2025).
Inclusion of 29 primary studies meeting specific criteria.
Thematic analysis of extracted data following PRISMA-Scoping guidelines.

Main Results:

LLMs utilized for patient education, self-management, and emotional support (62%, 28%, 14%).
LLMs also supported practitioners in clinical decision-making and predictions (28%, 21%).
Key challenges include response accuracy (62%), data limitations (21%), usability (31%), and ethical/regulatory concerns (35%).

Conclusions:

LLMs demonstrate significant potential for improving chronic disease management for both patients and healthcare providers.
Addressing accuracy, data, usability, and ethical issues is vital for safe and equitable LLM deployment.
Future research should focus on integrating LLMs with existing technologies and developing robust evaluation frameworks.