RadBERT: Adapting Transformer-based Language Models to Radiology

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To investigate if tailoring a transformer-based language model to radiology is beneficial for radiology natural language processing (NLP) applications. This retrospective study presents RadBERT, a family of bidirectional encoder representations from transformers-based language models adapted for radiology.