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Artificial General Intelligence (AGI) models show promise in medical imaging but face domain-specific challenges. This review explores their applications, evolution, and future directions in healthcare.

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Area of Science:

Artificial Intelligence in Medicine
Medical Imaging Analysis
Healthcare Technology Integration

Background:

Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs), excel in general tasks.
Direct application of these models to specialized fields like medical imaging presents significant challenges due to domain complexity.

Purpose of the Study:

To review the potential applications of AGI models in medical imaging and healthcare.
To examine the evolution, implementation, and challenges of AGI in the medical sector.
To identify future research directions for AGI in medical imaging.

Main Methods:

Comprehensive review of AGI models, focusing on LLMs, Large Vision Models, and Large Multimodal Models.
Analysis of key features, enabling techniques, and implementation roadmaps for AGI in medicine.
Summary of current applications, potential, and challenges.

Main Results:

AGI models offer significant potential for revolutionizing medical imaging and healthcare.
Key challenges include adapting general models to the unique complexities of the medical domain.
Current applications are emerging, with substantial future potential identified.

Conclusions:

AGI models, particularly LLMs, Large Vision Models, and Large Multimodal Models, are poised to transform medical imaging.
Addressing domain-specific challenges is crucial for successful implementation.
Further research is needed to fully realize the potential of AGI in healthcare.