Radiation Oncologists' Perceptions of Adopting an Artificial Intelligence-Assisted Contouring Technology : Model Development and Questionnaire Study
©Huiwen Zhai, Xin Yang, Jiaolong Xue, Christopher Lavender, Tiantian Ye, Ji-Bin Li, Lanyang Xu, Li Lin, Weiwei Cao, Ying Sun. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.09.2021..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
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| Enthalten in: |
Journal of medical Internet research - 23(2021), 9 vom: 30. Sept., Seite e27122 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhai, Huiwen [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 28.10.2021 Date Revised 17.11.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.2196/27122 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a ©Huiwen Zhai, Xin Yang, Jiaolong Xue, Christopher Lavender, Tiantian Ye, Ji-Bin Li, Lanyang Xu, Li Lin, Weiwei Cao, Ying Sun. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.09.2021. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a intension | |
| 650 | 4 | |a resistance | |
| 650 | 4 | |a technology acceptance model | |
| 700 | 1 | |a Yang, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Xue, Jiaolong |e verfasserin |4 aut | |
| 700 | 1 | |a Lavender, Christopher |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Tiantian |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ji-Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Lanyang |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Weiwei |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Ying |e verfasserin |4 aut | |
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