A Road Map for Translational Research on Artificial Intelligence in Medical Imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop
Publication date: Available online 28 May 2019Source: Journal of the American College of RadiologyAuthor(s): Bibb Allen, Steven E. Seltzer, Curtis P. Langlotz, Keith P. Dreyer, Ronald M. Summers, Nicholas Petrick, Danica Marinac-Dabic, Marisa Cruz, Tarik K. Alkasab, Robert J. Hanisch, Wendy J. Nilsen, Judy Burleson, Kevin Lyman, Krishna KandarpaAbstractAdvances in machine learning in medical imaging are occurring at a rapid pace in research laboratories both at academic institutions and in industry. Important artificial intelligence (AI) tools for diagnostic imaging include algorithms for disease detection and classification, image optimization, radiation reduction, and workflow enhancement. Although advances in foundational research are occurring rapidly, translation to routine clinical practice has been slower. In August 2018, the National Institutes of Health assembled multiple relevant stakeholders at a public meeting to discuss the current state of knowledge, infrastructure gaps, and challenges to wider implementation. The conclusions of that meeting are summarized in two publications that identify and prioritize initiatives to accelerate foundational and translational research in AI for medical imaging. This publication summarizes key priorities for translational research developed at the workshop including: (1) creating structured AI use cases, defining and highlighting clinical challenges potentially solvable by AI; (2) establishing methods to encourage data sharing f...
Source: Journal of the American College of Radiology - Category: Radiology Source Type: research