Large-Scale Foundation Models for Radiological Image Analysis: Clinical Applications, Technical Challenges, and Future Directions.
Authors
Affiliations (10)
Affiliations (10)
- Mayo Clinic, Rochester, MN, USA. [email protected].
- University of Wisconsin-Madison, Madison, USA.
- Stony Brook University, Stony Brook, USA.
- Mayo Clinic, Rochester, MN, USA.
- University of Pisa, Pisa, Italy.
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Surgery, Mayo Clinic, Rochester, MN, USA.
- Vanderbilt University, Nashville, USA.
- MedAiConsult, Cleveland, USA.
- University of Pittsburgh, Pittsburgh, PA, USA.
Abstract
Foundation models represent a transformative paradigm in radiological image analysis, offering large-scale, versatile systems that move beyond traditional task-specific AI approaches. While prior surveys have summarized their capabilities in medical imaging, a critical gap remains-the translation from research innovation to sustainable clinical radiology practice. This review directly addresses the implementation gap by presenting subspecialty-specific deployment strategies, real-world performance benchmarks from clinical settings, and practical PACS/RIS integration protocols informed by deployments across 15 healthcare systems. Rather than cataloging model architectures or theoretical advances, we focus on how radiology departments can effectively and successfully implement foundation models in practice, providing actionable, evidence-based guidance for clinical teams responsible for making adoption and deployment decisions. We explore architectures ranging from Vision Transformers to multimodal systems, their pre-training strategies, and adaptation techniques for medical imaging tasks. Clinical applications across multiple imaging modalities have demonstrated significant advances in lesion detection, disease classification, and automated reporting. Despite this progress, challenges remain in achieving robust clinical validation, navigating regulatory approval, and addressing ethical implementation. In this review, we evaluate key performance metrics and examine integration challenges, with particular attention to issues of data heterogeneity, interpretability, and computational efficiency. This review uniquely integrates an imaging informatics perspective, framing foundation models within the full lifecycle of medical imaging data from acquisition to clinical integration. We also highlight emerging solutions and outline future directions, including next-generation architectures, applications in personalized medicine, and strategies to expand accessibility in resource-constrained settings. We further highlight practical solutions for computational efficiency and accessibility, such as edge computing and model compression, vital for broader institutional adoption. This review provides clinical teams-radiologists, administrators, and imaging informatics specialists-with actionable guidance on foundation model implementation, while also serving as a reference for researchers exploring applied AI translation in radiology.