Promoting health equity through linguistic justice: mitigating embedded bias of large language models

The application of Large Language Models (LLMs) in healthcare prompts critical reflection on the impact of linguistic justice on health equity. Disparities in data availability across languages during the training and evaluation phases of LLMs result in significant performance variations. These disparities risk reinforcing existing inequities in healthcare access and outcomes. This study outlines strategies for integrating linguistic justice into LLM assessment frameworks, advancing health equity and global health.