Using Large Language Models for Chronic Disease Management Tasks: Scoping Review
LLMs show promising potential for enhancing chronic disease management across patient and practitioner-centered tasks. However, challenges related to accuracy, data scarcity, usability, and ethical concerns must be addressed to ensure patient safety and equitable use. Future studies should prioritize the integration of LLMs with low-resource platforms, wearable and mobile technologies, developing culturally and age-appropriate interfaces, and establishing robust regulatory and evaluation frameworks to support safe, effective, and inclusive use in health care.
Authors
- Paul Mukasa Ssemaluulu (ORCID: https://orcid.org/0000-0003-4912-3485)
- Nasser Kimbugwe (ORCID: https://orcid.org/0000-0002-8839-3069)
- Hasifah Kasujja Namatovu (ORCID: https://orcid.org/0000-0002-0090-5032)
- Christopher Garimoi Orach (ORCID: https://orcid.org/0000-0002-5209-5529)
- Peter Waiswa (ORCID: https://orcid.org/0000-0001-9868-0515)
- Jianquan Ouyang (ORCID: https://orcid.org/0000-0002-7518-5156)
- Henry Mukalazi Serugunda (ORCID: https://orcid.org/0000-0002-9060-0389)
Institutions
- Hunan University of Science and Technology (CN)
- Kabale University (UG)
- Xiangtan University (CN)
- Makerere University (UG)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-09-29
- DOI
- https://doi.org/10.2196/66905
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.31