Large-Scale Foundation Models for Radiological Image Analysis: Clinical Applications, Technical Challenges, and Future Directions.
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 fr...
| Publicado en: | Journal of Imaging Informatics in Medicine p. 1 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190842362&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190842362 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jan2026 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 190842362 10.1007/s10278-025-01823-w 190842362 ppf: 1 formats: tig: atl: Large-Scale Foundation Models for Radiological Image Analysis: Clinical Applications, Technical Challenges, and Future Directions. aug: au: Singh, Yashbir Unal, Orhan Ali, Farzana Salehi, Sara Kuanar, Shiba Elhanashi, Abdussalam Hathaway, Quincy A. Chaudhary, Ashok Huo, Yuankai Younis, Khaled Gu, Quincy Keishing, Varekan Wei, Yujia Zhang, Lili Erickson, Bradley J. affil: Mayo Clinic sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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