Open AccessRadiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring Technology: Model Development and Questionnaire Study
Author(s) -
Huiwen Zhai,
Xin Yang,
Jiaolong Xue,
Christopher Lavender,
Tiantian Ye,
JiBin Li,
Lanyang Xu,
Li Lin,
Weiwei Cao,
Yu Sun
Publication year - 2021
Publication title -
jmir. journal of medical internet research/journal of medical internet research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.446
H-Index - 142
eISSN - 1439-4456
pISSN - 1438-8871
DOI - 10.2196/27122
Subject(s) - contouring , unified theory of acceptance and use of technology , expectancy theory , theory of planned behavior , context (archaeology) , psychology , applied psychology , structural equation modeling , technology acceptance model , resistance (ecology) , health belief model , medicine , clinical psychology , usability , social psychology , artificial intelligence , computer science , nursing , health education , public health , machine learning , human–computer interaction , paleontology , ecology , computer graphics (images) , control (management) , biology
Background An artificial intelligence (AI)–assisted contouring system benefits radiation oncologists by saving time and improving treatment accuracy. Yet, there is much hope and fear surrounding such technologies, and this fear can manifest as resistance from health care professionals, which can lead to the failure of AI projects. Objective The objective of this study was to develop and test a model for investigating the factors that drive radiation oncologists’ acceptance of AI contouring technology in a Chinese context. Methods A model of AI-assisted contouring technology acceptance was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model by adding the variables of perceived risk and resistance that were proposed in this study. The model included 8 constructs with 29 questionnaire items. A total of 307 respondents completed the questionnaires. Structural equation modeling was conducted to evaluate the model’s path effects, significance, and fitness. Results The overall fitness indices for the model were evaluated and showed that the model was a good fit to the data. Behavioral intention was significantly affected by performance expectancy ( β =.155; P =.01), social influence ( β =.365; P <.001), and facilitating conditions ( β =.459; P <.001). Effort expectancy ( β =.055; P =.45), perceived risk ( β =−.048; P =.35), and resistance bias ( β =−.020; P =.63) did not significantly affect behavioral intention. Conclusions The physicians’ overall perceptions of an AI-assisted 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 the Venkatesh UTAUT model applied to AI technology adoption among physicians in a Chinese context.