Interpretability, particularly in terms of human understandable concepts, is essential for building trust in machine learning models for disease classification. However, state-of-the-art image classifiers exhibit limited interpretability, posing a significant barrier to their acceptance in clinical practice. To address this, our work introduces two graph representations of the retinal vasculature, aiming to bridge the gap between high-performance classifiers and human-understandable interpretability concepts in ophthalmology. We use these graphs with the aim of training graph neural networks (GNNs) for disease staging. First, we formally and experimentally show that GNNs can learn known clinical biomarkers. In that, we show that GNNs can learn human interpretable concepts. Next, we train GNNs for disease staging and study how different aggregation strategies lead the GNN to learn more and less human interpretable features. Finally, we propose a visualization for integrated gradients on graphs, which allows us to identify if GNN models have learned human-understandable representations of the data. The code is available at github.com/luxtu/graphs_in_ophthalmology .