Today, the Journal of the American College of Radiology (JACR®) published a report detailing real-world artificial intelligence (AI) challenges and summarizing the priorities for translational research in AI for medical imaging to help accelerate the safe and effective use of AI in clinical practice. The four key priorities outlined include:
creating structured AI use cases, defining and highlighting clinical challenges potentially solvable by AI;
establishing methods to encourage data sharing for training and testing AI algorithms to promote generalizability to widespread clinical practice and mitigate unintended bias;
establishing tools for validation and performance monitoring for AI algorithms to facilitate regulatory approval; and
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developing standards and common data elements for seamless integration of AI tools into existing clinical workflows.
As part of a multi-stakeholder approach, part one of the road map published in Radiology outlined the challenges, opportunities and priorities for foundational research in AI for medical imaging. The two reports are the outcome of an August 2018 workshop convened by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) at the National Institutes of Health (NIH) in Bethesda, Md., to explore the future of AI in medical imaging. The NIH, the American College of Radiology (ACR), the Radiological Society of North America (RSNA) and The Academy for Radiology and Biomedical Imaging Research (The Academy) co-sponsored the workshop.
"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, workshop co-chair and chief medical officer of the ACR Data Science Institute®. “The workshop highlighted structured AI use case development, access to diverse sources of data for training AI models, multi-site algorithm validation and monitoring the performance of these models using real-world data from clinical use as ways to accelerate the widespread deployment and clinical use of AI algorithms to improve the care we provide our patients.”
"Radiology has transformed the practice of medicine in the past century, and AI has the potential to radically impact radiology in positive ways,” said 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.”
