Foundation Models for Medical Imaging: Status, Challenges, and Directions
🤖 AI Summary
The field of medical imaging has long relied on task-specific models, lacking foundational models that are generalizable across imaging modalities, anatomical structures, and diverse clinical tasks. This work systematically reviews the design principles, core technical components—including large-scale pretraining, cross-modal alignment, and transfer learning—and strategies for clinical adaptation of foundational models in medical imaging. For the first time, it establishes an integrated development framework encompassing technical, clinical, and ethical dimensions, emphasizing trustworthiness and responsible translation into practice. The study proposes a roadmap for developing general-purpose models that balance technical sophistication with clinical feasibility, offering clear guidance for future research and real-world deployment.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Foundation Models
Medical Imaging
Generalization
Clinical Translation
Trustworthiness
Innovation
Methods, ideas, or system contributions that make the work stand out.
Foundation Models
Medical Imaging
Model Generalization
Clinical Translation
Trustworthy AI
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