arXiv:2606. 01904v1 Announce Type: cross Abstract: The increasing application of Natural Language Processing (NLP) in healthcare demands language models specifically attuned to the complexities of clinical language.
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The paper introduces a web‑data curation recipe for pretraining medical encoders, addressing the scarcity of large, diverse corpora in dense‑terminology domains like medicine. It proposes two complementary techniques: medical‑term density filtering to select documents rich in medical terminology, and signal‑amplifying rephrasing that uses an LLM to rewrite documents into denser variants with broader entity contexts. Applied to French medical NLP, the recipe produces the FineMed corpus and the DoctoBERT encoder family, achieving state‑of‑the‑art results on the DrBenchmark public benchmark and a proprietary clinical NER task.
This study benchmarks transformer models for Bangla medical named entity recognition (NER), comparing BanglaBERT, multilingual BERT (mBERT), XLM‑RoBERTa, and GPT‑4o mini under zero‑shot and few‑shot prompting. Across a full test set of 3,179 samples, fine‑tuned XLM‑RoBERTa achieves a new state‑of‑the‑art F1‑score of 0.5959, while BanglaBERT lags with 0.4937, suggesting that domain diversity outweighs language specificity. The analysis shows high performance on Medicine and Specialist entities (F1 > 0.83) but lower accuracy on Symptoms (F1 0.4367), and demonstrates that fine‑tuned transformers outperform prompt‑only approaches by a factor of 3.76.
The paper investigates continued pretraining (CPT) of the Finnish BERT model (FinBERT) on a Finnish histopathological dataset, noting that CPT train‑time loss curves vary significantly across domains. Since the histopathology data lacks labels, the authors use public Finnish datasets as proxy data to examine whether CPT‑derived signals correlate with downstream classification gains. Their exploratory analysis finds that certain CPT features are associated with improved proxy classification performance, adding to the sparse literature on Finnish healthcare NLP.
arXiv:2604. 25374v2 Announce Type: replace-cross Abstract: Background: Dutch medical corpora are scarce, limiting NLP development.
arXiv:2608.29890v1 Announce Type: new Abstract: Biomedical Named Entity Recognition (NER) is fundamental to healthcare AI applications, including clinical decision support and medical information ext...
The paper examines how multilingual medical adaptation affects the internal representations of Whisper ASR models. By comparing various fine‑tuning strategies—zero‑shot decoding, English‑only, German‑only, two‑stage EN→EN+DE, and direct EN+DE fine‑tuning—it shows that fine‑tuning significantly improves performance, with the best model varying by setting. Layer‑wise encoder analysis reveals that English medical fine‑tuning drives the main representation shift, while multilingual continuation largely preserves the adapted space, and that domain and language signals remain recoverable across layers.