  Integrating deep learning with clinical expertise holds great potential for
addressing healthcare challenges and empowering medical professionals with
improved diagnostic tools. However, the need for annotated medical images is
often an obstacle to leveraging the full power of machine learning models. Our
research demonstrates that by combining synthetic images, generated using
diffusion models, with real images, we can enhance nonalcoholic fatty liver
disease (NAFLD) classification performance even in low-data regime settings. We
evaluate the quality of the synthetic images by comparing two metrics:
Inception Score (IS) and Fr\'{e}chet Inception Distance (FID), computed on
diffusion- and generative adversarial network (GAN)-generated images. Our
results show superior performance for the diffusion-generated images, with a
maximum IS score of $1.90$ compared to $1.67$ for GANs, and a minimum FID score
of $69.45$ compared to $100.05$ for GANs. Utilizing a partially frozen CNN
backbone (EfficientNet v1), our synthetic augmentation method achieves a
maximum image-level ROC AUC of $0.904$ on a NAFLD prediction task.
