We investigate how tokenization granularity affects the representation of medical terminology in language models. Prior work links tokenization granularity to downstream performance under contextualized settings for specifically pretrained and fine-tuned models. We instead ask whether this relationship already emerges at the level of isolated term representations across existing pretrained models. We introduce an intrinsic definition retrieval task using UMLS term-definition pairs, with comparison to WordNet. We show that despite substantially heavier fragmentation of medical terminology, the models remain relatively robust in maintaining semantic alignment between medical terms and their definitions. At the same time, tokenization granularity still correlates with retrieval performance, indicating that effects previously observed in downstream biomedical tasks are already reflected at the level of isolated term representations. Encoder models benefit primarily from whole-token preservation, while for decoder LLMs, tokenization effects emerge mainly at deeper retrieval ranks.

This paper explores the performance of multilingual models in the general domain on the clinical Question Answering (QA) task to observe their potential medical support for languages that do not benefit from the existence of clinically trained models. In order to improve the model’s performance, we exploit multilingual data augmentation by translating an English clinical QA dataset into six other languages. We propose a translation pipeline including projection of the evidences (answers) into the target languages and thoroughly evaluate several multilingual models fine-tuned on the augmented data, both in mono- and multilingual settings. We find that the translation itself and the subsequent QA experiments present a differently challenging problem for each of the languages. Finally, we compare the performance of multilingual models with pretrained medical domain-specific English models on the original clinical English test set. Contrary to expectations, we find that monolingual domain-specific pretraining is not always superior to general-domain multilingual pretraining. The source code is available at https://github.com/lanzv/Multilingual-emrQA

In this paper, we present our submission to the ArchEHR-QA 2025 shared task, which focuses on answering patient questions based on excerpts from electronic health record (EHR) discharge summaries. Our approach identifies essential sentences relevant to a patient’s question using a combination of few-shot inference with the Med42-8B model, cosine similarity over clinical term embeddings, and the MedCPT cross-encoder relevance model. Then, concise answers are generated on the basis of these selected sentences. Despite not relying on large language models (LLMs) with tens of billions of parameters, our method achieves competitive results, demonstrating the potential of resource-efficient solutions for clinical NLP applications.

Healthcare professionals often manually extract information from large clinical documents to address patient-related questions. The use of Natural Language Processing (NLP) techniques, particularly Question Answering (QA) models, is a promising direction for improving the efficiency of this process. However, document-level QA from large documents is often impractical or even infeasible (for model training and inference). In this work, we solve the document-level QA from clinical reports in a two-step approach: first, the entire report is split into segments and for a given question the most relevant segment is predicted by a NLP model; second, a QA model is applied to the question and the retrieved segment as context. We investigate the effectiveness of heading-based and naive paragraph segmentation approaches for various paragraph lengths on two subsets of the emrQA dataset. Our experiments reveal that an average paragraph length used as a parameter for the segmentation has no significant effect on performance during the whole document-level QA process. That means experiments focusing on segmentation into shorter paragraphs perform similarly to those focusing on entire unsegmented reports. Surprisingly, naive uniform segmentation is sufficient even though it is not based on prior knowledge of the clinical document’s characteristics.