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ACR Releases Second Research Road Map on Medical Imaging AI

By HospiMedica International staff writers
Posted on 24 Jun 2019
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Image: New research outlines the challenges, opportunities and priorities for foundational research in AI for medical imaging (Photo courtesy of ABM).
Image: New research outlines the challenges, opportunities and priorities for foundational research in AI for medical imaging (Photo courtesy of ABM).
The Journal of the American College of Radiology (JACR) has 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 report is the second part of a road map published in Radiology outlining 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) Bethesda, MA, USA} to explore the future of AI in medical imaging.

The second report outlines four key priorities, namely 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 developing standards and common data elements for seamless integration of AI tools into existing clinical workflows.

“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.”

“Our companion paper gave a roadmap to advance foundational machine learning research. But for foundational research to benefit patients, novel algorithms must be evaluated and deployed in a safe and effective manner. This new roadmap paper gives guidance for the clinical translation of AI innovation,” said Curtis P. Langlotz, MD, PhD, report co-author and RSNA board liaison for information technology and annual meeting. “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.”

Related Links:
National Institute of Biomedical Imaging and Bioengineering


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