RaTE-NER dataset is a large-scale, radiological named entity recognition (NER) dataset, including 13,235 manually annotated sentences from 1,816 reports within the MIMIC-IV database, that spans 9 imaging modalities and 23 anatomical regions, ensuring comprehensive coverage.

Additionally, we further enriched the dataset with 33,605 sentences from the 17,432 reports available on Radiopaedia, by leveraging GPT-4 and other medical knowledge libraries to capture intricacies and nuances of less common diseases and abnormalities. We manually labeled 3,529 sentences to create a test set.