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

TL;DR

This paper reviews the rise of foundation models in medical imaging, discussing their design principles, applications, challenges, and future directions to facilitate trustworthy clinical deployment.

Contribution

It provides a comprehensive, technically grounded overview of medical imaging foundation models, highlighting current status, challenges, and future research directions.

Findings

Foundation models enable versatile applications across modalities and tasks.

Challenges include ensuring trustworthiness and responsible clinical translation.

The review offers a roadmap for future development of medical imaging FMs.

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.