Fig. 1. The self-supervised model classifies pathologies without training on any labelled samples.

a, Training pipeline. The model learns features from raw radiology reports, which act as a natural source of supervision. b, Prediction of pathologies in a chest X-ray image. For each pathology, we generated a positive and negative prompt (such as ‘consolidation’ versus ‘no consolidation’). By comparing the model output for the positive and negative prompts, the self-supervised method computes a probability score for the pathology, and this can be used to classify its presence in the chest X-ray image.