arXiv:2606.01904v1 Announce Type: new Abstract: The increasing application of Natural Language Processing (NLP) in healthcare demands language models specifically attuned to the complexities of clinical language. This work introduces KliniskVestBERT, a suite of three BERT-based encoder models pre-trained on a substantial corpus of real-world, de-identified Norwegian clinical texts from Helse Vest. We continue pretraining existing language models Nb-BERT-large, NorBERT3-large, and ModernBERT on our specialized clinical dataset. This dataset is based on a representative population of Helse Vest
The increasing maturity of large language models and the urgent need for domain-specific applications in healthcare are driving specialized model development.
This development highlights the growing trend of nations and regions building custom AI models on their data to address specific linguistic and regulatory needs, especially in sensitive sectors like healthcare.
The availability of KliniskVestBERT provides a specialized tool for NLP in Norwegian clinical texts, improving accuracy and potentially accelerating medical research and patient care within Norway.
- · Norwegian healthcare providers
- · NLP researchers in Norway
- · Patients in Norway
- · Helse Vest
- · General-purpose language models in clinical settings
- · Developers reliant solely on English-centric clinical NLP
Improved efficiency and accuracy of clinical NLP applications in Norway.
Potential for other nations or linguistic groups to develop their own sovereign clinical AI models.
Increased global fragmentation of AI models, each specialized for local language, culture, and regulatory frameworks.
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Read at arXiv cs.CL