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

arXiv 2602.15913 ↗First posted 17 Feb 2026eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 174 of its 276 references that are in the map. First posted on arXiv 17 Feb 2026.

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174 of 276 references.

References · 174

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