Australian perspectives on artificial intelligence in medical imaging. Issue 3 (15th April 2022)

• Record Type: Journal Article
• Authors: Currie, Geoffrey; Nelson, Tarni; Hewis, Johnathan; Chandler, Amanda; Spuur, Kelly; Nabasenja, Caroline; Thomas, Cate; Wheat, Janelle
• Is Part Of: Journal of medical radiation sciences. Volume 69:Issue 3(2022)
• Page Start: 282
• Page End: 292
• Publication Date: 2022-04-15
• Subjects: artificial intelligence -- convolutional neural network -- deep learning -- machine learning -- nuclear medicine -- radiography; Radiology, Medical -- Periodicals; Radiology, Medical -- Australia -- Periodicals; Radiology, Medical -- New Zealand -- Periodicals; Radiotherapy -- Periodicals; Diagnostic imaging -- Periodicals; 616
• DOI: 10.1002/jmrs.581
• Languages: English
• ISSNs: 2051-3895

Abstract

Abstract: Introduction: While artificial intelligence (AI) and recent developments in deep learning (DL) have sparked interest in medical imaging, there has been little commentary on the impact of AI on imaging technologists. The aim of this survey was to understand the attitudes, applications and concerns among nuclear medicine and radiography professionals in Australia with regard to the rapidly emerging applications of AI. Methods: An anonymous online survey with invitation to participate was circulated to nuclear medicine and radiography members of the Rural Alliance in Nuclear Scintigraphy and the Australian Society of Medical Imaging and Radiation Therapy. The survey invitations were sent to members via email and as a push via social media with the survey open for 10 weeks. All information collected was anonymised and there is no disclosure of personal information as it was de‐identified from commencement. Results: Among the 102 respondents, there was a high level of acceptance of lower order tasks (e.g. patient registration, triaging and dispensing) and less acceptance of high order task automation (e.g. surgery and interpretation). There was a low priority perception for the role of AI in higher order tasks (e.g. diagnosis, interpretation and decision making) and high priority for those applications that automate complex tasks (e.g. quantitation, segmentation, reconstruction) or improve image quality (e.g. dose / noise reduction and pseudo CT for attenuation correction). Medico‐legal, ethical, diversity and privacy issues posed moderate or high concern while there appeared to be no concern regarding AI being clinically useful and improving efficiency. Mild concerns included redundancy, training bias, transparency and validity. Conclusion: Australian nuclear medicine technologists and radiographers recognise important applications of AI for assisting with repetitive tasks, performing less complex tasks and enhancing the quality of outputs in medical imaging. There are concerns relating to ethical aspects of algorithm development and implementation. Abstract : While artificial intelligence (AI) and deep learning (DL) have sparked interest in medical imaging, there has been little commentary on the impact of AI on imaging technologists. Australian nuclear medicine technologists and radiographers recognise important applications of AI for assisting with repetitive tasks, performing menial tasks and enhancing the quality of outputs in medical imaging. There are concerns relating to ethical aspects of algorithm development and implementation.