Leveraging Virtual Containers for High-Powered, Collaborative AI Research in Radiology.

Area of Science:

• Radiology
• Artificial Intelligence
• Computer Science

Background:

• Radiologists encounter significant obstacles in adopting artificial intelligence (AI) models.
• Discrepancies in hardware, software, and GPU accessibility hinder AI implementation.
• The dissemination and use of complex AI models are complicated by their size and dependencies.

Purpose of the Study:

• To address the challenges faced by radiologists in utilizing AI models.
• To introduce virtual containers as a solution for AI compatibility and deployment issues.
• To highlight the features and practical applications of virtual containers in radiology AI.

Main Methods:

• Virtual containers are software tools that bundle code and dependencies.
• They ensure identical program execution across diverse computing environments.
• An applied use case demonstrates their utility in AI model development.

Main Results:

• Virtual containers offer solutions for hardware and software compatibility issues.
• They simplify the process of accessing and running AI models for radiologists.
• Key features include enhanced compatibility, versatility, and portability.

Conclusions:

• Virtual containers effectively overcome barriers to AI adoption in radiology.
• They provide a standardized and simplified approach to using complex AI models.
• This technology facilitates broader integration of AI tools in clinical practice.