Foundation Models for Medical Imaging: Status, Challenges, and Directions
This addresses the need for general-purpose models in medical imaging to improve adaptability and clinical translation, but it is incremental as a review paper.
The paper reviews the shift in medical imaging from task-specific networks to foundation models, synthesizing design principles, applications, and challenges to provide a roadmap for developing powerful, versatile, and trustworthy models for clinical use.
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.