Domain-Specific Multilingual Strategies for Medical NLP: A Cross-Lingual Analysis of Orthographic and Phonemic...

Medical natural language processing (NLP) has greatly benefited from the increasing availability of electronic health records (EHRs) across multiple languages. However, most available resources are heavily biased toward English, restricting the development of non-English medical NLP models and limiting their broader adoption in healthcare AI.In general domain NLP, monolingual models trained on high-resource European languages often outperform multilingual models. However, this paper takes a domain-specific perspective, highlighting the unique linguistic characteristics of medical terminology. Using a diverse corpus of medical texts in English, Italian, Spanish, and French, we pretrain RoBERTa-based models in both monolingual and multilingual settings. Contrary to conventional NLP trends, our results suggest that multilingual training enhances cross-lingual knowledge transfer and can even outperform monolingual models in medical domains. Through a comparative analysis of orthographic and phonemic representations, we find evidence suggesting that the strong Latin roots of medical terminology may facilitate effective knowledge sharing among languages with similar scripts.These findings indicate that orthographically focused multilingual strategies could provide a more robust framework for modeling specialized medical terminology. In particular, multilingual training that capitalizes on script similarities appears to enable richer information utilization, which may contribute to improved medical NLP performance across languages.Clinical relevance- Advancing multilingual medical language models can enhance clinical decision support and expand access to critical healthcare information across linguistically diverse communities.