  Medical imaging has revolutionized disease diagnosis, yet the potential is
hampered by limited access to diverse and privacy-conscious datasets.
Open-source medical datasets, while valuable, suffer from data quality and
clinical information disparities. Generative models, such as diffusion models,
aim to mitigate these challenges. At Stanford, researchers explored the utility
of a fine-tuned Stable Diffusion model (RoentGen) for medical imaging data
augmentation. Our work examines specific considerations to expand the Stanford
research question, Could Stable Diffusion Solve a Gap in Medical Imaging Data?
from the lens of bias and validity of the generated outcomes. We leveraged
RoentGen to produce synthetic Chest-XRay (CXR) images and conducted assessments
on bias, validity, and hallucinations. Diagnostic accuracy was evaluated by a
disease classifier, while a COVID classifier uncovered latent hallucinations.
The bias analysis unveiled disparities in classification performance among
various subgroups, with a pronounced impact on the Female Hispanic subgroup.
Furthermore, incorporating race and gender into input prompts exacerbated
fairness issues in the generated images. The quality of synthetic images
exhibited variability, particularly in certain disease classes, where there was
more significant uncertainty compared to the original images. Additionally, we
observed latent hallucinations, with approximately 42% of the images
incorrectly indicating COVID, hinting at the presence of hallucinatory
elements. These identifications provide new research directions towards
interpretability of synthetic CXR images, for further understanding of
associated risks and patient safety in medical applications.
