Foundation Models for Medical Imaging: Status, Challenges, and Directions
#foundation models #medical imaging #deep learning #task‑specific networks #cross‑modality #clinical tasks #design principles #evaluation metrics #interdisciplinary collaboration
📌 Key Takeaways
- Foundation Models shift medical imaging from task‑specific to general‑purpose models.
- They enable adaptation across imaging modalities, anatomies, and clinical tasks.
- The review synthesizes FM design principles, applications, and challenges in three axes.
- It highlights the growing landscape and strategic directions for future research.
- The article underscores the need for standardized evaluation and interdisciplinary collaboration.
📖 Full Retelling
Researchers worldwide have outlined how *Foundation Models* (FMs) are transforming medical imaging by moving the field from narrowly trained, task‑specific networks toward large, general‑purpose models. Leveraging cross‑modal, cross‑anatomical, and cross‑clinical capabilities, these models can be adapted to diverse imaging tasks and modalities. This review, published by the medical imaging community in early 2026, synthesizes current FM design principles, showcases real‑world applications, and proposes forward‑looking challenges and opportunities.
🏷️ Themes
Foundation Models, Medical Imaging, Cross‑modal Adaptation, Design Principles, Future Directions
Entity Intersection Graph
No entity connections available yet for this article.
Original Source
arXiv:2602.15913v1 Announce Type: cross
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, th
Read full article at source