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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Leveraging large language models to bridge the digital divide in cardiovascular health research
Daniel Seung Kim1,2,3,4,5, Ahmed Eltahir1, Sriya Mantena1
1Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA USA.
Abstract:
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.
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