Radiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring Technology: Model Development and Questionnaire Study
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Abstract
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.
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Several studies have demonstrated the applicability of the UTAUT in investigating the implementation of AI-CDSSs.
“Several studies have already demonstrated the applicability of the UTAUT in investigating the implementation of AI-CDSSs [12-17].”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [15]
Zhai et al reported positive relationships between perceived usefulness and social influence with the intention to use AI-assisted contouring technology among radiation oncologists.
“Zhai et al [15] reported positive relationships between perceived usefulness and social influence with the intention to use AI-assisted contouring technology among radiation oncologists.”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [15]
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- Authors
- Zhai, H., Yang, X., Xue, J., Lavender, C., Ye, T., Li, J.-B., Xu, L., Lin, L., Cao, W., & Sun, Y.
- Affiliations
- Sun Yat-sen University (CN)
- Sun Yat-sen University Cancer Center (CN)
- Southern University of Science and Technology (CN)
- Corresponding author
- Sun, Ying
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2021-09-30
- Volume
- 23
- Issue
- 9
- Pages
- e27122
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC8517819
- OpenAlex
- W3186667780
- Cited by
- 57
- References
- 49
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 12 · 2023: 12 · 2022: 6 · 2021: 1
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (72072191)
- Sun Yat-sen University
- National Key Research and Development Program of China (2020YFC1316900)
- Sun Yat-sen University Cancer Center
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- technology acceptance, radiation oncologists, UTAUT, risk perception, resistance to technology adoption, behavioral intention, performance expectancy, social influence, facilitating conditions, Chinese physicians
- MeSH terms
- Radiation Oncologists, Artificial Intelligence, Humans, Perception, Surveys and Questionnaires, Intention
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