Radiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring Technology: Model Development and Questionnaire Study

Autor: Huiwen Zhai, Xin Yang, Jiaolong Xue, Christopher Lavender, Tiantian Ye, Ji-Bin Li, Lanyang Xu, Li Lin, Weiwei Cao, Ying Sun
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of Medical Internet Research, Vol 23, Iss 9, p e27122 (2021)
Druh dokumentu: article
ISSN: 1438-8871
DOI: 10.2196/27122
Popis: BackgroundAn 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. ObjectiveThe 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. MethodsA 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. ResultsThe 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
Databáze: Directory of Open Access Journals
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