Osteoarthritis is a chronic joint disease in which the protective cartilage between bones deteriorates over time, leading to pain, stiffness, and reduced mobility. Diagnosis is a time-consuming and somewhat subjective process. To address this challenge, machine learning techniques can be applied. However, training supervised models on medical images is often challenging because of the limited availability of labeled training data. Self-supervised methods, which pretrain models to learn useful features without labels, offer a potential solution to this issue. In this paper, we explore the use of Generative Adversarial Networks (GANs) as a pre-training step for osteoarthritis diagnosis. The first step is the training of a GAN on a semi-public dataset of x-ray images. In the second stage, we explore different strategies for fine-tuning the discriminator model to diagnose osteoarthritis. Our experiments suggest that while GAN-based pre-training offers slight improvements over purely supervised approaches, the performance gains remain modest.