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

Abstract

Numerous obstacles confront radiologists interested in the use of artificial intelligence (AI) models within the field of radiology. For example, discrepancies between the radiologist's and an AI developer's hardware and software specifications pose a substantial hindrance to using AI models. Additionally, accessing and using GPU computers can lead to compatibility issues and add to these challenges. Finally, the dissemination of AI models and the ability to download preexisting AI models are not simple tasks due to the size and complexity of most programs. Virtual containers offer a solution to such compatibility issues and provide a simplified way for radiologists to use AI models. Virtual containers are software tools that bundle code, required programs, and necessary software packages to ensure that a program runs identically for all users, regardless of their computing environment. This article outlines the features of virtual containers (compatibility, versatility, and portability) and highlights an applied use case for virtual containers in the development of an AI model.

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