Artificial Intelligence and the Medical Radiation Profession: How Our Advocacy Must Inform Future Practice

Abstract

There is no escaping the fact that academics are devoting unrelenting attention to the impact artificial intelligence will have on health care. Radiological and radiation oncology organizations worldwide are devoting their time and resources to ensure their members are both informed and prepared for the inevitable changes to the respective professions. This commentary provides an overview of how artificial intelligence will affect medical radiation professions of both diagnostic and radiation therapy streams. It outlines how these professions can play an active role in ensuring optimal outcomes for the well-being of both the workforce and the patients.

Cautiously optimistic: A survey of radiation oncology professionals’ perceptions of automation in radiotherapy planning

“…Participants in a previous study advocated that they should be involved in guiding the introduction of AI, rather than passively accepting new roles or the elimination of jobs [13]. Radiation oncology professions can play an active role in ensuring optimal outcomes for the well-being of both the workforce and the patients [20].…”

Artificial intelligence in diagnostic imaging: impact on the radiography profession

“…Significantly, the radiographer voice debating the professional issues surrounding the adoption of AI technologies and increasing image acquisition automation has, until recently, 25,26 been noticeably quiet within both professional and industrial literature. It is uncertain whether this represents resigned acquiescence to the inevitable march of AI technologies, professional struggles with comprehending the enormity of the potential impact of AI or professional apathy as a learned defence to change (or fear of) 27 but it contrasts starkly to the debates and arguments of radiologists when the notion of diagnostic AI became popular as an automated solution to radiology reporting backlogs.…”

Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study

“…5 AI in medical imaging gained more widespread recognition with the introduction of complex computer systems and development of artificial neural network systems as well as machine learning technologies in the 1980s. 5 Although image interpretation is possibly the most well-researched task of AI in medical imaging in an attempt to improve the detection of pathologies 3,4,6 , current studies are focussed on its application beyond this scope to broadly support imaging professionals in achieving optimal results with ease. 1,[7][8][9][10][11] Particularly, AI tools are being used as clinical decision support enhancers and supportive systems for improving imaging workflow, image acquisition, disease identification, research efficiency, radiation exposures and delivering high-quality care.…”