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Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology – national survey

Authors :
Kristen Wong
Francois Gallant
Ewa Szumacher
Source :
Journal of Medical Imaging and Radiation Sciences. 52:44-48
Publication Year :
2021
Publisher :
Elsevier BV, 2021.

Abstract

Background Artificial Intelligence (AI) is making a continuous progression into the field of Radiation Oncology in Canada and globally. While this field continues to evolve, there is no clear understanding about how radiation oncologists, radiation therapists, medical physicists and radiation trainees perceive AI and its’ impact on radiation oncology as a discipline. The purpose of this study was to investigate the perception of these four Canadian professional groups about AI. and how AI will affect radiation oncology as a specialty. Methods Following an in-depth scientific review of the existing literature, a 29 Likert-scale questionnaire was developed using Google Survey. The questionnaire was piloted and distributed through national organizations including the Canadian Association for Radiation Oncology (CARO), the Canadian Association of Medical Radiation Therapy (CAMRT) and the Canadian Organization of Medical Physicists (COMP), initially in February, and again between March and June 2020. The results were analyzed using descriptive statistics. Results 159 responses were received from 10 Canadian provinces. Knowledge about AI was moderate with an average of 5/10, but 91% responded interest in learning more about it. The negative implications of AI were related to fear of losing jobs and shift of practice. The majority of participants agreed AI would positively impact on patient treatment. Conclusion Radiation oncology professionals believe AI will be an important part of patient treatment in their future practices. The fear about AI may be mitigated with further education programs about AI, which can gain more confidence in the acceptance of AI.

Details

ISSN :
19398654
Volume :
52
Database :
OpenAIRE
Journal :
Journal of Medical Imaging and Radiation Sciences
Accession number :
edsair.doi.dedup.....3cd5f7843c0371e6984a76c78b8e5817