NIH and radiology societies map path for translational research on AI in medical imaging

A new report from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), provides a roadmap for translational research on artificial intelligence (AI) in medical imaging, according to a National Institutes of Health release.  The report, published in the Journal of the American College of Radiology, identifies research priorities that leverage big data, the cloud, and machine learning for augmenting clinicians’ image planning and use to make diagnoses or assess patients’ responses to therapy.

The authors suggest that the application of AI can impact the entire radiology process, from the clinical decision to perform diagnostic imaging, to preparation of patients for procedures, to conducting the scan, to interpretation of imaging results, and to the management of workflow in radiology departments, reports NIH.

“Radiology has transformed the practice of medicine in the past century, and AI has the potential to radically impact radiology in positive ways,” stated Krishna Kandarpa, MD, PhD, 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.”

NIH says the report and companion summarize conclusions from an August 2018 workshop co-organized by NIH, the Radiologic American College of Radiology (ACR), the Radiological Society of North America (RSNA), and The Academy for Radiology and Biomedical Imaging Research. The first report, published in April, maps a path forward for foundational research in AI and the second report focuses on translational research necessary to deliver AI to clinical practice.

The companion reports — co-authored by government, industry, academia and radiology specialty society leaders — have identified and prioritized initiatives to accelerate foundational and translational research in AI for medical imaging. The authors suggest that radiologists should take the lead in identifying the most important areas for AI development.

Key priorities identified include:

·   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.

“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.