Foundation models in medical imaging: A review

Foundation models (FMs), which are large-scale architectures pretrained on diverse datasets, are rapidly reshaping artificial intelligence (AI). In medical imaging, these models provide unified methods for learning rich visual and multimodal representations, facilitating accurate diagnoses, efficient clinical workflows, and equitable access to healthcare. This review surveys the FMs in medical imaging and summarizes key insights for future research. It situates medical FMs in the broader context of generalist AI, categorizing them from vision-derived adaptations to modality-specific and emerging general-purpose systems. This review systematically compares core architectures, pre-training objectives, and clinical performance, highlighting improvements in adaptability, robustness, and data efficiency. Persistent challenges, such as limited data availability, privacy regulations, interpretability issues, and real-world deployment constraints, were examined alongside practical solutions, including self-supervised learning, federated training, and parameter-efficient fine-tuning. Future directions discussed include multimodal integration, lightweight inference suitable for edge devices, and rigorous validation that adheres to regulatory standards. By consolidating the current knowledge and identifying open research questions, this review offers clear guidance for researchers and clinicians aiming to integrate FMs into routine medical practice, laying the groundwork for subsequent detailed explorations. • Foundation models improves generalization in medical imaging tasks. • Vision-, modality-, and general-purpose models are systematically compared. • FMs mitigate data scarcity via self-supervised and federated learning. • Clinical applications include diagnosis, prognosis, and workflow automation. • Future work includes multimodal fusion, deployment, and validation.