Advancements in Multilingual Biomedical Natural Language Processing: exploring Large Language Models for Named Entity Recognition and Linking

Abstract Objective Named Entity Recognition (NER) and Biomedical Entity Linking (BEL) are essential for transforming unstructured Electronic Health Records (EHRs) into structured information. However, tools for these tasks are limited in non-English biomedical texts such as Dutch and Italian. This study investigates the use of prompt-based learning with Large Language Models (LLMs) to perform multilingual NER and BEL using minimal domainspecific data, while addressing annotation preservation during corpus translation. Methods An English-annotated corpus from the ShARe/CLEF dataset was translated into Dutch and Italian using a strategy that embeds annotations directly into the text prior to translation and retrieves them afterwards. GPT-4o was applied in zero-shot and few-shot settings to extract biomedical entities, which were then mapped to Unified Medical Language System Concept Unique Identifiers using contextual word embeddings. Performance was evaluated with precision, recall, and F1-score, and compared with goldstandard clinician annotations. Results The multilingual NER pipeline achieved strong performance, with an overall F1-score of 0.98 across languages. BEL experiments showed reliable entity normalization, with an overall accuracy of 0.91 and a mean reciprocal rank of 0.95. The combined performance of the NER and BEL achieved 0.90 supporting the utility of LLMs in standardizing biomedical concepts across languages. Conclusion Prompt-based LLMs can effectively perform NER and BEL in languages with less annotated resources, even with limited annotated training data. The proposed annotation-preserving translation method, combined with generative and discriminative LLM capabilities, provides a scalable approach to multilingual clinical information extraction. These findings highlight the potential for broader adoption of LLM-based natural language processing systems to support multilingual healthcare data harmonization. Graphical Abstract Highlights This study shows the feasibility of using prompt-based learning with large language models (LLMs) to perform multilingual named entity recognition (NER) and biomedical entity linking (BEL) in Dutch and Italian, two languages with less annotated resources. An annotation-preserving translation strategy was proposed to adapt the ShARe/CLEF eHealth corpus, enabling consistent evaluation across English, Dutch, and Italian without loss of gold-standard annotations. The multilingual NER pipeline achieved strong overall performance (F1-score: 0.89), while BEL experiments showed reliable entity normalization (F1-score: 0.64, MRR: 0.68) to standardized clinical concepts. The approach highlights the potential of generative and discriminative LLM capabilities for scalable multilingual clinical information extraction, supporting broader European initiatives for cross-lingual health data harmonization.