Allen says the aim of DSI structured use cases is to bring together multiple institutions to create data sets for a particular use case that can be trained and tested with more technical, geographic and patient diversity than those created at single institutions, thereby reducing algorithm bias.

“The next step is for medical imaging algorithm developers and data scientists to become familiar with the AC DSI use cases and follow this method so that algorithms can move beyond being intellectual curiosities that pop up in medical journals or the press and deliver on their promise to safely and effectively improve patient care,” he said. “There is no other large scale framework that can move promising medical imaging AI ideas to effective clinical practice tools so safely and efficiently.”


ACR DSI announced its intention to build a use cases framework last November at RSNA for the first time as part of its objective to convert artificial intelligence from a concept into an everyday practice in radiology. To complete its endeavor, the association teamed up with partners, including the Medical Image Computing and Computer Assistance Intervention Society (MICCAI).

Its first uses cases were released in July for feedback.

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