RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports

Radiology reports are essential for clinical care but pose challenges for
automated processing due to their unstructured nature. Existing datasets like
RadGraph-1.0 focus narrowly on chest X-rays (CXR), limiting their
applicability. We introduce RadGraph-XL, a large-scale, expert-annotated
dataset of 2,300 radiology reports with over 410,000 labeled entities and
relations, spanning four anatomy-modality pairs: chest computed tomography
(CT), abdomen/pelvis CT, brain magnetic resonance imaging (MR), and CXR.

Each report is annotated by board-certified radiologists using a detailed
schema that captures observations, anatomical references, and their
relationships. A novel post-processing step identifies measurement-related
entities, a clinically valuable category. Trained models using RadGraph-XL
outperform prior methods and GPT-4, and generalize well to out-of-domain data
such as deep vein thrombosis (DVT) ultrasound reports.

RadGraph-XL is released publicly with models and annotations to support
applications in clinical natural language processing (NLP), medical imaging
artificial intelligence, and foundation model evaluation, setting a new
benchmark for structured information extraction in radiology.