Computer Vision News Computer Vision News 32 Grand Challenge - Medical Imaging Medical Imaging and Data Resource Center (MIDRC) XAI Challenge This Grand Challenge is organized and overseen by the Medical Imaging and Data Resource Center’s (MIDRC) Grand Challenge Working Group (GCWG). Key members of the GCWG are Karen Drukker (left), a Research Associate Professor at the University of Chicago, Lubomir Hadjiiski (center), a Professor of Radiology at the University of Michigan, and Sam Armato (right), a Professor in the Department of Radiology and Medical Physics at the University of Chicago. They are all here with Emily Townley, the MIDRC Program Manager at the American Association of Physicists in Medicine (AAPM), to tell us more. The XAI Challenge aims to address a critical issue in the field of AI and medical imaging: the need for explainable AI. With a focus on chest radiographs, challenge participants must identify pulmonary disease, specifically pneumonia, and explain the reasoning behind their AI’s predictions. They are not only trying to get the correct answers for the cases in the challenge but also to demonstrate that their AI system is getting the right answer for the right reason, which is a challenge in itself in the world of AI. Alongside the concept of explainable AI, there is a complementary idea of trustworthy AI. The challenge comes at a time when AI in medicine often operates as a black box, making it difficult for radiologists to trust its decisions. The team wants to develop methods that allow radiologists to trust their output in a way that benefits patient care. “The better the decisions, the more sure everyone is of what’s going on, the earlier the patient can be treated, which results in better outcomes,” Karen tells us. “Fewer mistakes and
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