Constructing an Artificial Intelligence-Driven Multilingual Medical Health Education Chatbot with Domain-Specific Medical Knowledge.

Abstract

Language barriers remain a critical challenge in health care delivery, particularly for migrant and multicultural populations. This study presents the design, implementation, and evaluation of an artificial intelligence (AI)-driven multilingual medical health education chatbot to enhance health care communication and accessibility. The proposed system introduces a user-state-driven multi-layer logical architecture, which enables efficient multilingual interaction by dynamically routing user queries to language-specific processing modules. In addition, a hybrid retrieval-generative framework is developed to combine expert-curated health education content with conversational AI (ChatGPT), ensuring both information reliability and interaction flexibility. The system is deployed on the LINE platform and evaluated using the System Usability Scale, Net Promoter Score, and Customer Satisfaction Score with 85 participants from diverse linguistic and cultural backgrounds. The results demonstrate satisfactory usability and user acceptance, with consistent performance observed across nationality groups, supporting the system's cross-cultural applicability. Overall, the findings indicate that the proposed architecture provides a scalable, efficient, and practical solution for multilingual health care communication, reducing language barriers and improving patient understanding in real-world clinical environments.