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

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.