Medical Imaging Foundation Models

Medical imaging foundation models aim to learn reusable features from large collections of images, scans, and sometimes paired clinical reports, then apply them to tasks such as diagnosis, segmentation, measurement, and report generation. Common approaches include self-supervised learning, masked-image prediction, vision-language contrastive learning, and generative models, with both 2D and volumetric methods for modalities such as radiology, MRI, ultrasound, pathology, and retinal imaging. Researchers adapt these models through fine-tuning, lightweight adapters such as LoRA, frozen-feature probes, or prompts, and assess them on classification, segmentation, and cross-center benchmarks. A central tension is whether broad pretraining reliably helps specific clinical tasks: results vary by modality and task, and robustness to shifts in scanners, populations, and institutions remains essential to establish before deployment.