An AI-driven bilingual clinical language processing framework for English-Yoruba medical text and voice translation and understanding

Abstract

The research came up with a bilingual natural language processing (NLP) model of a patient-focused healthcare system that is geared towards improving healthcare communication in low-resource settings, based on both the English and Yoruba languages. The system combines multilingual transformer-based text processing, speech-to-text and text-to-speech modules, retrieval-augmented response generation, and offline-compatible deployment to provide culturally adaptive and personalized health information. Assessment was made based on conventional performance measures such as accuracy, cultural relevance, readability, and response quality through pilot testing and comparison against other existing bilingual models of healthcare delivery. The experimental findings indicated a general classification accuracy of 91%, which revealed a high language interpretation and response-generating ability. The system also achieved cultural relevance of 40% by adapting to indigenous healthcare communication requirements and readability of 91% by simplifying complex medical information into patient-friendly explanations. Analysis indicated that linguistic inclusiveness and accessibility were enhanced as compared to the current monolingual practices. The results confirm that bilingual NLP models have the potential to significantly reduce communication barriers, enhance patient comprehension, and promote equitable healthcare practices in bilingual and underserved communities. Comparative analysis confirmed that the proposed bilingual NLP model outperforms existing monolingual and rule-based systems in linguistic inclusiveness and accessibility. The results validate that bilingual NLP models can significantly reduce communication barriers, enhance patient comprehension, and promote equitable healthcare delivery in multilingual, underserved communities.