Welcome to ACR AI-LAB™ Community

Explore ACR AI-LAB™ Ecosystem, Demonstrations, Commercial Participation and Community resources.

AI LAB™ Ecosystem

The paper provides an outline and framework for the decisions, infrastructure, and steps involved in bringing AI into your organization. We encourage all interested parties to review the draft, including radiologists, data scientists and others in the radiology community with an interest in AI technology.

Please read the draft and submit your comments by July 20, 2019

picture_as_pdf ACR AI-LAB™ Ecosystem Draft

ACR AI-LAB™ Demonstrations

Working with the ACR AI-LAB™ platform, seven institutions will demonstrate the process of using transfer learning to share an Artificial Intelligence (AI) model between sites, permitting an AI model developed at one location to be shared and tuned at other locations.

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The ACR partnered with NVIDIA and four healthcare organizations across the globe: OSU, DASA, Stanford, and MGH, to carry out this pilot project. From this experiment, we have shown that federated learning can work as a method for more and more healthcare organizations to participate in the creation of high quality AI models.

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ACR AI-LAB™ Commercial Participation

The AI-Assisted Annotation SDK enables application developers to integrate the deep learning tools built into the SDK with their existing medical imaging applications, such as MITK. This is accomplished using a simple API and requires no prior deep learning knowledge.

picture_as_pdf ACR AI-LAB™ Commercial Participation