  AI Foundation models are gaining traction in various applications, including
medical fields like radiology. However, medical foundation models are often
tested on limited tasks, leaving their generalisability and biases unexplored.
We present RayDINO, a large visual encoder trained by self-supervision on 873k
chest X-rays. We compare RayDINO to previous state-of-the-art models across
nine radiology tasks, from classification and dense segmentation to text
generation, and provide an in depth analysis of population, age and sex biases
of our model. Our findings suggest that self-supervision allows patient-centric
AI proving useful in clinical workflows and interpreting X-rays holistically.
With RayDINO and small task-specific adapters, we reach state-of-the-art
results and improve generalization to unseen populations while mitigating bias,
illustrating the true promise of foundation models: versatility and robustness.
