Fundamentals of Foundation Models in Radiological Image Processing: Opportunities, Limitations, and Clinical Case...

Area of Science:

• Artificial intelligence in medical imaging
• Foundation models (FMs) for radiological analysis
• Deep learning in radiology

Background:

• Current AI in radiology primarily uses task-specific convolutional neural networks (CNNs).
• Foundation models (FMs) are large-scale, pretrained models adaptable to various radiological tasks with minimal fine-tuning.
• FMs enable novel applications like zero-shot segmentation and clinical context integration.

Purpose of the Study:

• Introduce the principles of foundation models in radiological image analysis.
• Discuss the technical and economic implications of FMs in radiology.
• Illustrate how FMs can transform radiological image analysis with clinical examples.

Main Methods:

• Review of foundation model principles and applications in medical imaging.
• Analysis of technical, economic, and clinical implications.
• Presentation of clinically relevant case studies.

Main Results:

• Foundation models offer flexible application across radiological tasks, unlike specialized CNNs.
• FMs show potential to enhance radiological and clinical performance, especially in complex or data-limited scenarios.
• Commercial products are beginning to integrate FM-based approaches, replacing specialized models.

Conclusions:

• Foundation models represent a significant advancement in AI for radiology.
• Careful validation, explainability, and regulatory compliance are crucial for clinical integration.
• FMs hold the potential to revolutionize radiological image analysis and clinical practice.