Foundation Models for Medical Imaging: Status, Challenges, and Directions
Abstract
Foundation models (FMs) are rapidly reshaping medical imaging, shifting the field from narrowly trained, task-specific networks toward large, generalpurpose 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: underlying core components of FMs, medical imaging applications of FMs, and forward-looking perspectives on 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.
Fields of Study & Subjects
Optical Imaging and Spectroscopy Techniques
Advanced Radiotherapy Techniques
Radiomics and Machine Learning in Medical Imaging
Publication details
- Journal / source
- IEEE Transactions on Medical Imaging
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- PP
- Pages
- 1–1
- Publication date
- 1 January 2026
- Language
- EN