Leveraging AI algorithms to meet clinical needs in radiology

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Use of artificial intelligence (AI)-based techniques continues to expand in radiology and other areas of medicine. In fact, the American College of Radiology (ACR) is collaborating with the Medical Image Computing and Computer Assistance Intervention (MICCAI) Society to develop AI algorithms to better meet the clinical needs of radiologists.

Radiology has played a leading role in the application of advanced technology in medicine, and AI represents another important area of innovation and opportunity, according to Mike Tilkin, ACR's Chief Information Officer and Executive Vice President. “Although it’s still early, we believe AI algorithms will be useful in a variety of areas throughout the imaging life-cycle and will help radiologists be more efficient and provide better patient care," Tilkin said.

The ACR, which has decades of experience creating imaging standards and a history of promoting imaging informatics solutions (i.e., DICOM), is also actively creating use cases for imaging AI. The group will be working with MICCAI under this memorandum of understanding to leverage this knowledge base in MICCAI’s imaging AI competitions. 

In addition, ACR will work with MICCAI to promote learning on a global scale, including an upcoming keynote address from ACR’s incoming board chair, Dr. Geraldine McGinty, at MICCAI’s upcoming annual meeting in Granada, Spain.

“Working together, our organisations can help promote learning in a scientifically-rigorous manner, target solutions that have the greatest clinical impact, and promote standards that encourage a useful clinical workflow,” said Bibb Allen Jr., MD, FACR, and Chief Medical Officer, ACR Data Science Institute (ACR DSI). The ACR DSI is actively working on technically-oriented use cases (TOUCH-AI) which will help algorithm vendors identify and target areas that have the greatest clinical impact, as well as strategies to ensure appropriate validation pre-deployment (CERTIFY-AI) and ongoing monitoring while in the clinical setting (ASSESS-AI).

Image Credit: Pixabay 

Published on : Wed, 6 Jun 2018


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