"The scientific challenges and opportunities of AI in medical imaging are profound, but quite different from those facing AI generally. Our goal was to provide a blueprint for professional societies, funding agencies, research labs, and everyone else working in the field to accelerate research toward AI innovations that benefit patients," said the report's lead author, Curtis P. Langlotz, M.D., Ph.D. Dr. Langlotz is a professor of radiology and biomedical informatics, director of the Center for Artificial Intelligence in Medicine and Imaging, and associate chair for information systems in the Department of Radiology at Stanford University, and RSNA Board Liaison for Information Technology and Annual Meeting.
Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification and radiogenomics. Machine learning algorithms will transform clinical imaging practice over the next decade. Yet, machine learning research is still in its early stages.
"RSNA's involvement in this workshop is essential to the evolution of AI in radiology," said Mary C. Mahoney, M.D., RSNA Board of Directors Chair. "As the Society leads the way in moving AI science and education forward through its journals, courses and more, we are in a solid position to help radiologic researchers and practitioners more fully understand what the technology means for medicine and where it is going." In the report, the authors outline several key research themes, and describe a roadmap to accelerate advances in foundational machine learning research for medical imaging.
MEDICA-tradefair.com; Source: Radiological Society of North America
