A new report identifies research priorities to leverage advanced computing technologies to bring artificial intelligence to clinical practice.
The report, recently published in the Journal of the American College of Radiology, seeks to provide a roadmap for translational research on AI in medical imaging.
The new report includes contributions from the National Institute of Biomedical Imaging and Bioengineering—part of the National Institutes of Health—as well as key radiological professional organizations, including the Academy for Radiology and Biomedical Imaging Research, the American College of Radiology and the Radiological Society of North America.
The most recent report is a companion to one published in April and summarizes conclusions from an August 2018 workshop co-organized by the National Institutes of Health and the radiology groups.
The latest report discusses the best path forward to leverage big data, the cloud and machine learning to augment clinicians’ image planning and use to make diagnoses or assess patients’ responses to therapy.
“Radiology has transformed the practice of medicine in the past century, and AI has the potential to radically impact radiology in positive ways,” says Krishna Kandarpa, MD, co-author of the report and director of research sciences and strategic directions at NIBIB.
“This roadmap is a timely survey and analysis by experts at federal agencies and among our industry and professional societies that will help us take the best advantage of AI technologies as they impact the medical imaging field,” he adds.
“Together, these two connected roadmaps show us how AI not only will transform the work of radiologists and other medical imagers, but also will enhance the delivery of care throughout the clinical environment,” adds Curtis P. Langlotz, MD, also a co-author of the report and RSNA board liaison.
In this latest report, the authors identified several key priorities:
· Structured AI use cases. In software development, use cases define who will use a system and for what specific goal. AI use cases should define and highlight clinical challenges potentially solvable by AI.
· Data sharing. Researchers should establish methods to encourage data sharing for training and testing AI algorithms to promote generalizability to widespread clinical practice and minimize unintended bias.
· Tools for validation and performance monitoring of AI algorithms to facilitate regulatory approval.
· Standards and common data elements for seamless integration of AI tools into existing clinical workflows.
The report establishes that an important goal of the resulting roadmap is to grow an ecosystem—facilitated by professional societies, industry and government agencies—that will enable robust collaborations between practicing clinicians and AI researchers to advance foundational and translational research relevant to medical imaging.
“Although advances in foundational research are occurring rapidly, translation to routine clinical practice has been slower because we must ensure AI in medical imaging is useful, safe, effective, and easily integrated into existing radiology workflows before they can be used in routine patient care,” said Bibb Allen, MD, report co-author and chief medical officer of the ACR Data Science Institute.
For reprint and licensing requests for this article, click here.