Foundation Models for Medical Imaging: Status, Challenges, and Directions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910025426927616 |
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| author | Niu, Chuang Wu, Pengwei De Man, Bruno Wang, Ge |
| author_facet | Niu, Chuang Wu, Pengwei De Man, Bruno Wang, Ge |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15913 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Foundation Models for Medical Imaging: Status, Challenges, and Directions Niu, Chuang Wu, Pengwei De Man, Bruno Wang, Ge Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Foundation Models for Medical Imaging: Status, Challenges, and Directions |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.15913 |