The Application of Foundation Models in Radiology: Bridging Images, Reports, and Beyond

The use of AI in radiology is rapidly transitioning towards the use of foundation models that learn universal representations from large-scale imaging and multimodal clinical data. In this review, we outline the key technical components of these models, including self-supervised encoders, fusion modules for feature alignment, and task-specific decoders, and further summarize the recent work in three categories: 1) image-only models, trained on millions of unlabeled radiology scans to enable robust transfer learning with minimal annotation, 2) Chest X-ray image-report models, which leverage large chest X-ray-reported corpora for joint visual and textual embedding, and 3) image-report models for other modalities, which fuse volumetric images with structured reports or clinical metadata. We further discuss the relevant evaluation strategies, including vision-centric, language-centric, and benchmark-based metrics, and outline approaches for clinical validation. Finally, we highlight the persistent challenges in the application of these models, and propose future directions for multimodal integration and human-AI collaboration to advance personalized radiology.