Transforming Healthcare Dialogue with LLMs: A Multilingual Approach

Abstract

This research paper explores the integration of Large Language Models (LLMs) in transforming multilingual healthcare dialogues. LLMs have the potential to bridge language barriers between patients and healthcare providers, enabling inclusive communication and improving clinical outcomes. The paper presents a multilingual framework that leverages LLMs for real-time language translation, intent recognition, and culturally sensitive communication to facilitate effective interactions in diverse healthcare settings.The study also examines key technical and ethical challenges, including dialectal variations, model bias, data privacy, and regulatory compliance. To address these challenges, the proposed framework incorporates mitigation strategies such as fine-tuning on domain-specific medical corpora, integrating cultural ontologies, and deploying privacy-preserving AI models. The research aims to support equitable, accurate, and effective multilingual communication, ultimately enhancing the quality and accessibility of healthcare services across global healthcare systems.