Radiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring Technology: Model Development and Questionnaire Study
| Autor: | Li Lin, Christopher Lavender, Xin Yang, Weiwei Cao, Huiwen Zhai, Tiantian Ye, Ying Sun, Jibin Li, Lanyang Xu, Jiaolong Xue |
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| Rok vydání: | 2021 |
| Předmět: |
Expectancy theory
Original Paper technology acceptance model business.industry Radiation Oncologists Health Informatics Context (language use) Intention Unified theory of acceptance and use of technology Affect (psychology) Structural equation modeling resistance Risk perception intension Artificial Intelligence Surveys and Questionnaires Health care Humans Perception Technology acceptance model Artificial intelligence business Psychology |
| Zdroj: | Journal of Medical Internet Research |
| ISSN: | 1438-8871 |
| DOI: | 10.2196/27122 |
| Popis: | 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 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. |
| Databáze: | OpenAIRE |
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