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Australian perspectives on artificial intelligence in medical imaging

Australian perspectives on artificial intelligence in medical imaging IntroductionWhile artificial intelligence (AI) and more recent developments in deep learning (DL) have sparked clinical and research interest in medical imaging (radiology and nuclear medicine), a number of expert commentators, including Geoffrey Hinton, have predicted that AI would make radiologists redundant.1 A more realistic perspective might predict a change in the way some tasks are performed.1–3 There has been little commentary on the impact of AI in medical imaging on non‐medical personnel like imaging technologists. Indeed, a number of AI tools directly impact the imaging technologist interface. With the changing AI landscape, there is a need to understand the perspectives of the imaging technologists with respect to challenges, role and opportunity of AI.Hardy and Harvey4 identify acceptance of automated technology in radiography at the price of erosion of core skills; improved efficiency coming at the cost of increased workload and radiographer burnout. They raise concerns that the emergence of AI on top of these automations undermines the role and responsibilities of the radiographer. While it is conceivable that an AI system be designed that simply requires a “concierge” to direct the patient to the x‐ray room for more basic procedures, this would be very difficult to implement outside the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Radiation Sciences Wiley

Australian perspectives on artificial intelligence in medical imaging

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References (28)

Publisher
Wiley
Copyright
Copyright © 2022 Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology
ISSN
2051-3895
eISSN
2051-3909
DOI
10.1002/jmrs.581
Publisher site
See Article on Publisher Site

Abstract

IntroductionWhile artificial intelligence (AI) and more recent developments in deep learning (DL) have sparked clinical and research interest in medical imaging (radiology and nuclear medicine), a number of expert commentators, including Geoffrey Hinton, have predicted that AI would make radiologists redundant.1 A more realistic perspective might predict a change in the way some tasks are performed.1–3 There has been little commentary on the impact of AI in medical imaging on non‐medical personnel like imaging technologists. Indeed, a number of AI tools directly impact the imaging technologist interface. With the changing AI landscape, there is a need to understand the perspectives of the imaging technologists with respect to challenges, role and opportunity of AI.Hardy and Harvey4 identify acceptance of automated technology in radiography at the price of erosion of core skills; improved efficiency coming at the cost of increased workload and radiographer burnout. They raise concerns that the emergence of AI on top of these automations undermines the role and responsibilities of the radiographer. While it is conceivable that an AI system be designed that simply requires a “concierge” to direct the patient to the x‐ray room for more basic procedures, this would be very difficult to implement outside the

Journal

Journal of Medical Radiation Sciences – Wiley

Published: Sep 1, 2022

Keywords: artificial intelligence; convolutional neural network; deep learning; machine learning; nuclear medicine; radiography

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