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[2602.15913] Foundation Models for Medical Imaging: Status, Challenges, and Directions
Summary
This article reviews the current landscape of foundation models (FMs) in medical imaging, discussing their design principles, applications, and future challenges, aiming to provide a roadmap for their responsible clinical integration.
Why It Matters
Foundation models are transforming medical imaging by enabling versatile applications across various tasks and modalities. Understanding their design and challenges is crucial for researchers and practitioners aiming to implement these technologies responsibly in clinical settings.
Key Takeaways
- Foundation models are shifting medical imaging from task-specific networks to adaptable, general-purpose models.
- The article outlines key design principles and applications of FMs in medical imaging.
- It highlights the importance of trustworthiness and responsible clinical translation of these models.
- Emerging challenges and opportunities for FMs in medical imaging are discussed.
- The review serves as a roadmap for future research and development in this area.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2602.15913 (eess) [Submitted on 17 Feb 2026] Title:Foundation Models for Medical Imaging: Status, Challenges, and Directions Authors:Chuang Niu, Pengwei Wu, Bruno De Man, Ge Wang View a PDF of the paper titled Foundation Models for Medical Imaging: Status, Challenges, and Directions, by Chuang Niu and 3 other authors View PDF 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. Subjects: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2602.15913 [eess.IV] (or arXiv:2602.15913v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2602.15913 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Chuang Niu [view email] [v1] Tue, 17 Feb 20...
