health life sciences medicine
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.
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
https://doi.org/10.13026/tp88-q278
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Radiology Report Generation Models Evaluation Dataset For Chest X-rays (RadEvalX) was accessed on DATE from https://registry.opendata.aws/rad-eval-x.
arn:aws:s3:::physionet-open/rad-eval-x/us-east-1aws s3 ls --no-sign-request s3://physionet-open/rad-eval-x/