Figure 1. Schematic diagram comparing foundation models with traditional artificial intelligence models, showing the benefits of generalisability, label efficiency and computational efficiency. Rather than training a new model for each task, a single foundation model is generalisable to multiple downstream tasks. By learning general representation from vast quantities of unlabelled data, foundation models require less labelled data for each task (size of green boxes). These fine-tuning stages are also computationally efficient compared with training models from scratch. FM, foundation model.