Health information for all: do large language models bridge or widen the digital divide?
Large language models have the potential to enhance equitable access to health information, but their poor performance in some languages could exacerbate the digital divide in healthcare, say <b>Arthur Tang and colleagues</b>
Authors
- Kin On Kwok (ORCID: https://orcid.org/0000-0002-2804-5433)
- Stanley Luong (ORCID: https://orcid.org/0000-0002-3303-2979)
- Wilson Tam (ORCID: https://orcid.org/0000-0003-0641-3060)
- Nhat Bui (ORCID: https://orcid.org/0009-0000-8532-7464)
- Giang Nguyen (ORCID: https://orcid.org/0000-0002-6769-0195)
- Arthur Tang (ORCID: https://orcid.org/0000-0002-6655-6883)
- Huy Quang Nguyen (ORCID: https://orcid.org/0000-0002-5114-2063)
- Neo Tung
Institutions
- RMIT Vietnam (VN)
- The University of Melbourne (AU)
- National University of Singapore (SG)
- Chinese University of Hong Kong (HK)
- Oxford University Clinical Research Unit (VN)
Publication Details
- Journal
- BMJ
- Published
- 2024-10-11
- DOI
- https://doi.org/10.1136/bmj-2024-080208
- Citations
- 15
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.80
Funders
- RMIT University
- Chinese University of Hong Kong