English

Foundation Models for Medical Imaging: Status, Challenges, and Directions

Image and Video Processing 2026-02-19 v1 Artificial Intelligence Computer Vision and Pattern Recognition

Abstract

Foundation models (FMs) are rapidly reshaping medical imaging, shifting the field from narrowly trained, task-specific networks toward large, general-purpose models that can be adapted across modalities, anatomies, and clinical tasks. In this review, we synthesize the emerging landscape of medical imaging FMs along three major axes: principles of FM design, applications of FMs, and forward-looking challenges and opportunities. Taken together, this review provides a technically grounded, clinically aware, and future-facing roadmap for developing FMs that are not only powerful and versatile but also trustworthy and ready for responsible translation into clinical practice.

Keywords

Cite

@article{arxiv.2602.15913,
  title  = {Foundation Models for Medical Imaging: Status, Challenges, and Directions},
  author = {Chuang Niu and Pengwei Wu and Bruno De Man and Ge Wang},
  journal= {arXiv preprint arXiv:2602.15913},
  year   = {2026}
}