Fine-tuning large language models for improved health communication in low-resource languages
This case study highlights the unique challenges faced by developing countries using low-resource languages. Initiatives are needed to emphasize efforts to bridge healthcare gaps in underserved areas and contribute to global health equity.
Authors
- Tuyen Thi Thanh Huynh (ORCID: https://orcid.org/0000-0003-2040-1971)
- Vo Nguyen Quoc Bao (ORCID: https://orcid.org/0000-0002-1791-6467)
- Van Nhiem Tran (ORCID: https://orcid.org/0000-0001-6941-0348)
- Nhat Bui (ORCID: https://orcid.org/0009-0000-8532-7464)
- Tom Huynh (ORCID: https://orcid.org/0000-0003-4852-8528)
- Giang T. Nguyen (ORCID: https://orcid.org/0000-0003-2031-9943)
- Huy Quang Nguyen (ORCID: https://orcid.org/0000-0002-4676-2997)
- Arthur Tang (ORCID: https://orcid.org/0000-0002-6655-6883)
- Luan Nguyen Quang Vo (ORCID: https://orcid.org/0000-0002-5937-6286)
- Nguyen Nguyen (ORCID: https://orcid.org/0009-0001-6520-3015)
- Minh Dinh (ORCID: https://orcid.org/0009-0000-3405-4638)
Institutions
- RMIT Vietnam (VN)
- Taiwan Forestry Research Institute (TW)
- Oxford University Clinical Research Unit (VN)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1016/j.cmpb.2025.108655
- Citations
- 14
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 34.73