Foundation Models in Medical Imaging -- A Review and Outlook

Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are pre-trained to learn general-purpose visual features that can later be adapted to specific clinical tasks with little additional supervision. In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology, drawing on evidence from over 150 studies. We explain the core components of FM pipelines, including model architectures, self-supervised learning methods, and strategies for downstream adaptation. We also review how FMs are being used in each imaging domain and compare design choices across applications. Finally, we discuss key challenges and open questions to guide future research.

@article{veldhuizen2025_2506.09095,
  title={ Foundation Models in Medical Imaging -- A Review and Outlook },
  author={ Vivien van Veldhuizen and Vanessa Botha and Chunyao Lu and Melis Erdal Cesur and Kevin Groot Lipman and Edwin D. de Jong and Hugo Horlings and Clárisa I. Sanchez and Cees G. M. Snoek and Lodewyk Wessels and Ritse Mann and Eric Marcus and Jonas Teuwen },
  journal={arXiv preprint arXiv:2506.09095},
  year={ 2025 }
}