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

Improving Nonalcoholic Fatty Liver Disease Classification Performance With Latent Diffusion Models