Radiology Report Generation Models Evaluation Dataset For Chest X-rays (RadEvalX)

The Radiology Report Generation Models Evaluation Dataset For Chest X-rays
(RadEvalX) is publicly available and developed similarly to the ReXVal
dataset. Just like ReXVal, RadEvalX focuses on radiologist evaluations of
errors found in automatically generated radiology reports. The dataset
includes annotations from two board-certified radiologists, who identified
clinically significant and clinically insignificant errors across eight
different categories of errors. Compared to the ground-truth reports from the
IU-Xray dataset, the evaluations were done on candidate radiology reports. For
every 100 studies and corresponding ground-truth reports, the dataset contains
one report generated using the M2Tr model from the corresponding X-ray image.
The radiologists then annotated these reports. The primary purpose of this
dataset is to assess the correlation between automated metrics and human
radiologists' scoring, explore the limitations of automated metrics, and
develop a model-based automated metric. This dataset has been created to
support further research in medical artificial intelligence (AI), particularly
in the field of radiology.