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Related Concept Videos

  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Graphics, Augmented Reality And Games
  9. Computer Aided Design
  11. Leveraging Virtual Containers For High-powered, Collaborative Ai Research In Radiology.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Graphics, Augmented Reality And Games
  9. Computer Aided Design
  11. Leveraging Virtual Containers For High-powered, Collaborative Ai Research In Radiology.

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

Lucas Aronson1, John Garrett1, Andrew L Wentland1,2,3

  • 1Department of Radiology.

Journal of Computer Assisted Tomography
|January 6, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Radiologists face challenges using artificial intelligence (AI) due to hardware and software issues. Virtual containers solve these problems, simplifying AI model deployment and use in radiology.

Keywords:
artificial intelligencecomputed tomographyvirtual containers

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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.