  Rationale and Objectives: Medical artificial intelligence systems are
dependent on well characterised large scale datasets. Recently released public
datasets have been of great interest to the field, but pose specific challenges
due to the disconnect they cause between data generation and data usage,
potentially limiting the utility of these datasets.
  Materials and Methods: We visually explore two large public datasets, to
determine how accurate the provided labels are and whether other subtle
problems exist. The ChestXray14 dataset contains 112,120 frontal chest films,
and the MURA dataset contains 40,561 upper limb radiographs. A subset of around
700 images from both datasets was reviewed by a board-certified radiologist,
and the quality of the original labels was determined.
  Results: The ChestXray14 labels did not accurately reflect the visual content
of the images, with positive predictive values mostly between 10% and 30% lower
than the values presented in the original documentation. There were other
significant problems, with examples of hidden stratification and label
disambiguation failure. The MURA labels were more accurate, but the original
normal/abnormal labels were inaccurate for the subset of cases with
degenerative joint disease, with a sensitivity of 60% and a specificity of 82%.
  Conclusion: Visual inspection of images is a necessary component of
understanding large image datasets. We recommend that teams producing public
datasets should perform this important quality control procedure and include a
thorough description of their findings, along with an explanation of the data
generating procedures and labelling rules, in the documentation for their
datasets.
