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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.

Yueyan Bian^1,2,3, Jin Li^1,2,3, Chuyang Ye⁴

¹Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China.

Chinese Medical Journal

|February 26, 2025

Summary

Artificial intelligence (AI) in medical imaging uses task-specific and foundation models. Foundation models offer generalized learning for diverse applications, complementing existing AI approaches for improved clinical workflows.

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Area of Science:

Medical Imaging
Artificial Intelligence
Deep Learning

Background:

AI, especially deep learning, excels in medical imaging across various modalities.
Current AI models are often task-specific, limiting their scope.
Emerging foundation models learn generalized representations for broader applications.

Purpose of the Study:

Review clinical applications of task-specific and foundation AI models in medical imaging.
Highlight differences, complementarities, and clinical relevance.
Examine future research directions and challenges.

Main Methods:

Review of existing literature on task-specific and foundation AI models in medical imaging.
Comparative analysis of model capabilities and clinical integration.
Discussion of potential advancements and limitations.

Main Results:

Task-specific models are integrated into most medical image analyses.
Foundation models show potential for segmentation and classification, with broader future applications.
Task-specific and foundation models are complementary, not replacements.

Conclusions:

Foundation models represent a significant advancement in AI for medical imaging.
Both model types are crucial for current and future clinical workflows.
AI, particularly foundation models, promises breakthroughs in medical imaging and clinical practice.