Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 13, 2021
Date Accepted: Jul 27, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Radiation Oncologists' Perceptions of Adopting an AI-Assisted Contouring Technology
ABSTRACT
Background:
The AI assisted contouring system benefits radiation oncologists in terms of saving time and improving treatment accuracy. There is much hope and fear around such technologies, and it is the fear which can emanate as resistance for health care professionals, leading to the failure of AI projects.
Objective:
The objective of this research was to develop and test a model investigating the factors that drive radiation oncologists’ acceptance of the AI contouring technology in the Chinese context.
Methods:
A model of AI assisted contouring technology acceptance based on the UTAUT model adding variables of perceived risk and resistance was proposed in this study. The model included eight constructs with 29 questionnaire items and 307 respondents completed the questionnaires. Structural equation modeling (SEM) was conducted to evaluate item the model's path effects, significance and fitness.
Results:
The overall fitness indices for the model were evaluated showing the hypothesized model has a good fit to the data. Behavioral intention was significantly affected by performance expectancy (beta=.155; P=.014), social influence (beta=.365; P<.001), and facilitating conditions (beta=.459; P<.001). Effort expectancy (beta=.055; P=.450), perceived risk (beta=.-048; P=.348), and resistance bias (beta=–.020; P=.634) did not significantly affect behavioral intention.
Conclusions:
The physicians' overall perception of the AI assist technology for radiation contouring were high. Technology resistance among Chinese radiation oncologists was low and not related to behavioral intention. Not all of the factors in Venkatesh's UTAUT model apply to physicians' AI technology adoption in the Chinese context.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.