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Related Experiment Video

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Large-Scale Foundation Models for Radiological Image Analysis: Clinical Applications, Technical Challenges, and

Yashbir Singh1, Orhan Unal2, Farzana Ali3

  • 1Mayo Clinic, Rochester, MN, USA. Singh.yashbir@mayo.edu.

Journal of Imaging Informatics in Medicine
|January 13, 2026
PubMed
Summary
This summary is machine-generated.

This review provides practical guidance for implementing foundation models in clinical radiology, focusing on real-world deployment and integration strategies for improved patient care.

Keywords:
Deep learningDiagnostic imagingFoundation modelsMulti-modal learningRadiologyVision transformers

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging Informatics

Background:

  • Foundation models offer versatile AI for radiological image analysis, surpassing task-specific methods.
  • A gap exists in translating AI research into sustainable clinical radiology practice.

Purpose of the Study:

  • To bridge the gap between foundation model research and clinical radiology implementation.
  • To provide actionable, evidence-based guidance for adopting AI in radiology departments.

Main Methods:

  • Review of subspecialty-specific deployment strategies and real-world performance benchmarks.
  • Analysis of PACS/RIS integration protocols from 15 healthcare systems.
  • Evaluation of foundation model architectures, pre-training, and adaptation techniques.

Main Results:

  • Foundation models show advances in lesion detection, disease classification, and automated reporting across modalities.
  • Implementation challenges include clinical validation, regulatory approval, data heterogeneity, interpretability, and computational efficiency.
  • Successful integration requires attention to the full data lifecycle and practical solutions like edge computing.

Conclusions:

  • Foundation models hold significant promise for transforming radiology.
  • Actionable guidance is provided for radiologists, administrators, and informatics specialists for effective implementation.
  • Future directions include next-generation architectures, personalized medicine, and enhanced accessibility.