Generative AI for Multilingual Medical Question Answering System: Bridging Language Barriers in Healthcare

Abstract

The increasing dependence on digital platforms for healthcare information has raised the demand for smart systems that can handle medical queries in different languages. Conventional medical Question Answering (QnA) systems are mainly monolingual, single-language models that are English-centric and thus designed in a way that does not consider non-English-speaking populations. These systems, therefore, create a barrier to those who want to access the right healthcare knowledge. In this paper, we introduce a multilingual medical QnA system developed from datasets that have English, French, Spanish language texts. The system uses advanced Natural Language Processing (NLP) techniques, cross-lingual embeddings, and domain-specific corpora to maintain the contextual accuracy and coemprehensiveness of the text. The suggested approach to breaking down language barriers not only makes the healthcare access fairer but also supports practitioners who work in multilingual settings and facilitates the safer spread of medical knowledge.

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