Leveraging large language models to bridge the digital divide in cardiovascular health research
Advances in digital health exclude Hispanic/Latinx populations with limited English language proficiency, despite their high cardiovascular risk and smartphone usage. Large language models (LLMs) offer promising English-to-Spanish translational solutions, with comparable accuracy to professional medical translators. In this work, we present data and propose a hybrid workflow that combines automated LLM translation with professional review to reduce costs and improve Hispanic/Latinx inclusion in cardiovascular digital health research.
Authors
- Daniel Seung Kim (ORCID: https://orcid.org/0000-0003-2971-1909)
- Ahmed Eltahir (ORCID: https://orcid.org/0009-0008-1221-2007)
- Fátima Rodríguez (ORCID: https://orcid.org/0000-0002-5226-0723)
- Ritu Doijad
- Sriya Mantena
- Christian Matabang
Institutions
- University of Washington (US)
- Cardiovascular Institute of the South (US)
- Stanford Health Care (US)
- Stanford Medicine (US)
- Stanford University (US)
Publication Details
- Journal
- npj Cardiovascular Health
- Published
- 2025-12-02
- DOI
- https://doi.org/10.1038/s44325-025-00095-1
- Primary Topic
- Interpreting and Communication in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- American Diabetes Association
- American Heart Association
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- National Institutes of Health
- National Heart, Lung, and Blood Institute