  Since the release of the original CheXpert paper five years ago, CheXpert has
become one of the most widely used and cited clinical AI datasets. The
emergence of vision language models has sparked an increase in demands for
sharing reports linked to CheXpert images, along with a growing interest among
AI fairness researchers in obtaining demographic data. To address this,
CheXpert Plus serves as a new collection of radiology data sources, made
publicly available to enhance the scaling, performance, robustness, and
fairness of models for all subsequent machine learning tasks in the field of
radiology. CheXpert Plus is the largest text dataset publicly released in
radiology, with a total of 36 million text tokens, including 13 million
impression tokens. To the best of our knowledge, it represents the largest text
de-identification effort in radiology, with almost 1 million PHI spans
anonymized. It is only the second time that a large-scale English paired
dataset has been released in radiology, thereby enabling, for the first time,
cross-institution training at scale. All reports are paired with high-quality
images in DICOM format, along with numerous image and patient metadata covering
various clinical and socio-economic groups, as well as many pathology labels
and RadGraph annotations. We hope this dataset will boost research for AI
models that can further assist radiologists and help improve medical care. Data
is available at the following URL:
https://stanfordaimi.azurewebsites.net/datasets/5158c524-d3ab-4e02-96e9-6ee9efc110a1
Models are available at the following URL:
https://github.com/Stanford-AIMI/chexpert-plus
