Recommendations for Overcoming Linguistic Barriers in Healthcare: Challenges and Innovations in NLP for Haitian Creole

Abstract

Haitian Creole, spoken by millions in Haiti and its diaspora, remains underrepresented in Natural Language Processing (NLP) research, limiting the availability of effective translation tools. In Miami, a significant Haitian Creole-speaking population faces healthcare disparities exacerbated by language barriers. Existing translation systems fail to address key challenges such as linguistic variation within the Creole language, frequent code-switching, and the lack of standardized medical terminology. This work proposes a structured methodology for the development of an AI-assisted translation and interpretation tool tailored for patient-provider communication in a medical setting. To achieve this, we propose a hybrid NLP approach that integrates fine-tuned Large Language Models (LLMs) with traditional machine translation methods. This combination ensures accurate, context-sensitive translation that adapts to both formal medical discourse and conversational registers while maintaining linguistic consistency. Additionally, we discuss data collection strategies, annotation challenges, and evaluation metrics necessary for building an ethically designed, scalable NLP system. By addressing these issues, this research provides a foundation for improving healthcare accessibility and linguistic equity for Haitian Creole speakers.