US report provides guide for research on AI in medical imaging

29 May 2019 (Last Updated December 23rd, 2019 10:22)

The US Government, industry, academia and radiology societies have worked together to publish a report that provides a blueprint for translational research on artificial intelligence (AI) in medical imaging.

US report provides guide for research on AI in medical imaging
Artificial intelligence has an expanding role to play in medical imaging. Credit: NIBIB.

The US Government, industry, academia and radiology societies have worked together to publish a report that provides a blueprint for translational research on artificial intelligence (AI) in medical imaging.

The report focuses on research using big data, the Cloud and machine learning to enhance clinicians’ image planning and use for diagnoses or evaluation of patient response to therapy.

It follows an initial report published last month to map a path for foundational research in AI.

Report co-author and National Institute of Biomedical Imaging and Bioengineering (NIBIB) research sciences and strategic directions director Krishna Kandarpa said: “Radiology has transformed the practice of medicine in the past century, and AI has the potential to radically impact radiology in positive ways.

“This roadmap is a timely survey and analysis by experts at federal agencies and among our industry and professional societies will help us take the best advantage of AI technologies as they impact the medical imaging field.”

According to the authors, AI can be applied across the radiology process, including in clinical decision, diagnostic imaging, preparation of patients for procedures, scans, interpretation of imaging results and workflow management.

The new report covers algorithms to detect and classify diseases, optimise images, cut radiation and improve workflow.

The authors suggest that researchers should determine approaches to promote data sharing for training and testing AI algorithms in a bid to allow ‘generalisability’ and reduce unintended bias.

“AI has the potential to radically impact radiology in positive ways.”

Furthermore, tools to validate and monitor the performance of AI algorithms for regulatory approval, and standards and common data elements for integration of AI solutions into current clinical workflows have been suggested.

Academy of Radiology & Biomedical Imaging Research vice-president Eugene Pendergrass noted: “It will take a true public-private partnership to realise the tremendous potential contribution of AI to transform medical imaging, and this roadmap is the first step in that direction.”