"Barriers to market adoption include the utility of AI in clinical practice, a lack of clinical validation, the challenges of workflow integration, and limited reimbursement," said Parekh. "However, many of these barriers are increasingly being addressed, as there has been a rapid increase in the number of AI solutions receiving regulatory approval; AI imaging platforms are addressing the challenges of workflow integration and orchestration; and the potential of reimbursement for AI is on the horizon."

"From enhanced productivity and increased diagnostic accuracy, to more personalized treatment planning and improved clinical outcomes, AI will play a key role in enabling radiologists to meet the demands of their workload," said Parekh. "The increasing volume of diagnostic imaging procedures, exacerbated by the current backlog of imaging exams due to national lockdowns, coupled with the shortage of radiologists in many countries, will undoubtedly further increase the need for AI in radiology."

A number of AI applications are attracting attention and showing great potential for changing practices within medical imaging. For example, NYU Langone and Facebook have garnered much interest through a joint project in which they illustrate how AI can be used with less data to generate MR scans at a faster pace without compromising image quality and diagnostic accuracy.

“This study is an important step toward clinical acceptance and utilization of AI-accelerated MR scans because it demonstrates for the first time that AI-generated images are essentially indistinguishable in appearance from standard clinical MR exams, and are interchangeable in regard to diagnostic accuracy,” said Michael P. Recht, professor of radiology at NYU Langone and lead study author, in a statement following the publication of a study showing evidence of the benefits associated with the project.

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