  Biomedical imaging datasets are often small and biased, meaning that
real-world performance of predictive models can be substantially lower than
expected from internal testing. This work proposes using generative image
editing to simulate dataset shifts and diagnose failure modes of biomedical
vision models; this can be used in advance of deployment to assess readiness,
potentially reducing cost and patient harm. Existing editing methods can
produce undesirable changes, with spurious correlations learned due to the
co-occurrence of disease and treatment interventions, limiting practical
applicability. To address this, we train a text-to-image diffusion model on
multiple chest X-ray datasets and introduce a new editing method RadEdit that
uses multiple masks, if present, to constrain changes and ensure consistency in
the edited images. We consider three types of dataset shifts: acquisition
shift, manifestation shift, and population shift, and demonstrate that our
approach can diagnose failures and quantify model robustness without additional
data collection, complementing more qualitative tools for explainable AI.
