Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians

Anh Quynh Tran; Long Hoang Nguyen; Hao Si Anh Nguyen; Cuong Tat Nguyen; Linh Gia Vu; Melvyn Zhang; Thuc Minh Thi Vu; Son Hoang Nguyen; Bach Xuan Tran; Carl A. Latkin; Roger C.M. Ho; Cyrus S.H. Ho

doi:10.3389/fpubh.2021.755644

Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians

Anh Quynh Tran, Long Hoang Nguyen, Hao Si Anh Nguyen, Cuong Tat Nguyen, Linh Gia Vu, Melvyn Zhang, Thuc Minh Thi Vu, Son Hoang Nguyen, Bach Xuan Tran, Carl A. Latkin, Roger C.M. Ho, Cyrus S.H. Ho

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

Abstract

Background: This study aimed to develop a theoretical model to explore the behavioral intentions of medical students to adopt an AI-based Diagnosis Support System. Methods: This online cross-sectional survey used the unified theory of user acceptance of technology (UTAUT) to examine the intentions to use an AI-based Diagnosis Support System in 211 undergraduate medical students in Vietnam. Partial least squares (PLS) structural equational modeling was employed to assess the relationship between latent constructs. Results: Effort expectancy (β = 0.201, p < 0.05) and social influence (β = 0.574, p < 0.05) were positively associated with initial trust, while no association was found between performance expectancy and initial trust (p > 0.05). Only social influence (β = 0.527, p < 0.05) was positively related to the behavioral intention. Conclusions: This study highlights positive behavioral intentions in using an AI-based diagnosis support system among prospective Vietnamese physicians, as well as the effect of social influence on this choice. The development of AI-based competent curricula should be considered when reforming medical education in Vietnam.

Original language	English (US)
Article number	755644
Journal	Frontiers in Public Health
Volume	9
DOIs	https://doi.org/10.3389/fpubh.2021.755644
State	Published - Nov 26 2021

Keywords

artificial intelligence
diagnosis
intention
medical students
theoretical model

ASJC Scopus subject areas

Public Health, Environmental and Occupational Health

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10.3389/fpubh.2021.755644

Cite this

Tran, A. Q., Nguyen, L. H., Nguyen, H. S. A., Nguyen, C. T., Vu, L. G., Zhang, M., Vu, T. M. T., Nguyen, S. H., Tran, B. X., Latkin, C. A., Ho, R. C. M., & Ho, C. S. H. (2021). Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians. Frontiers in Public Health, 9, Article 755644. https://doi.org/10.3389/fpubh.2021.755644

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abstract = "Background: This study aimed to develop a theoretical model to explore the behavioral intentions of medical students to adopt an AI-based Diagnosis Support System. Methods: This online cross-sectional survey used the unified theory of user acceptance of technology (UTAUT) to examine the intentions to use an AI-based Diagnosis Support System in 211 undergraduate medical students in Vietnam. Partial least squares (PLS) structural equational modeling was employed to assess the relationship between latent constructs. Results: Effort expectancy (β = 0.201, p < 0.05) and social influence (β = 0.574, p < 0.05) were positively associated with initial trust, while no association was found between performance expectancy and initial trust (p > 0.05). Only social influence (β = 0.527, p < 0.05) was positively related to the behavioral intention. Conclusions: This study highlights positive behavioral intentions in using an AI-based diagnosis support system among prospective Vietnamese physicians, as well as the effect of social influence on this choice. The development of AI-based competent curricula should be considered when reforming medical education in Vietnam.",

keywords = "artificial intelligence, diagnosis, intention, medical students, theoretical model",

author = "Tran, {Anh Quynh} and Nguyen, {Long Hoang} and Nguyen, {Hao Si Anh} and Nguyen, {Cuong Tat} and Vu, {Linh Gia} and Melvyn Zhang and Vu, {Thuc Minh Thi} and Nguyen, {Son Hoang} and Tran, {Bach Xuan} and Latkin, {Carl A.} and Ho, {Roger C.M.} and Ho, {Cyrus S.H.}",

note = "Publisher Copyright: Copyright {\textcopyright} 2021 Tran, Nguyen, Nguyen, Nguyen, Vu, Zhang, Vu, Nguyen, Tran, Latkin, Ho and Ho.",

year = "2021",

month = nov,

day = "26",

doi = "10.3389/fpubh.2021.755644",

language = "English (US)",

volume = "9",

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AU - Tran, Anh Quynh

AU - Nguyen, Long Hoang

AU - Nguyen, Hao Si Anh

AU - Nguyen, Cuong Tat

AU - Vu, Linh Gia

AU - Zhang, Melvyn

AU - Vu, Thuc Minh Thi

AU - Nguyen, Son Hoang

AU - Tran, Bach Xuan

AU - Latkin, Carl A.

AU - Ho, Roger C.M.

AU - Ho, Cyrus S.H.

PY - 2021/11/26

Y1 - 2021/11/26

N2 - Background: This study aimed to develop a theoretical model to explore the behavioral intentions of medical students to adopt an AI-based Diagnosis Support System. Methods: This online cross-sectional survey used the unified theory of user acceptance of technology (UTAUT) to examine the intentions to use an AI-based Diagnosis Support System in 211 undergraduate medical students in Vietnam. Partial least squares (PLS) structural equational modeling was employed to assess the relationship between latent constructs. Results: Effort expectancy (β = 0.201, p < 0.05) and social influence (β = 0.574, p < 0.05) were positively associated with initial trust, while no association was found between performance expectancy and initial trust (p > 0.05). Only social influence (β = 0.527, p < 0.05) was positively related to the behavioral intention. Conclusions: This study highlights positive behavioral intentions in using an AI-based diagnosis support system among prospective Vietnamese physicians, as well as the effect of social influence on this choice. The development of AI-based competent curricula should be considered when reforming medical education in Vietnam.

AB - Background: This study aimed to develop a theoretical model to explore the behavioral intentions of medical students to adopt an AI-based Diagnosis Support System. Methods: This online cross-sectional survey used the unified theory of user acceptance of technology (UTAUT) to examine the intentions to use an AI-based Diagnosis Support System in 211 undergraduate medical students in Vietnam. Partial least squares (PLS) structural equational modeling was employed to assess the relationship between latent constructs. Results: Effort expectancy (β = 0.201, p < 0.05) and social influence (β = 0.574, p < 0.05) were positively associated with initial trust, while no association was found between performance expectancy and initial trust (p > 0.05). Only social influence (β = 0.527, p < 0.05) was positively related to the behavioral intention. Conclusions: This study highlights positive behavioral intentions in using an AI-based diagnosis support system among prospective Vietnamese physicians, as well as the effect of social influence on this choice. The development of AI-based competent curricula should be considered when reforming medical education in Vietnam.

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Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians

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