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

Abstract

<b>Background:</b> This study aimed to develop a theoretical model to explore the behavioral intentions of medical students to adopt an AI-based Diagnosis Support System. <b>Methods:</b> 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. <b>Results:</b> Effort expectancy (β = 0.201, <i>p</i> < 0.05) and social influence (β = 0.574, <i>p</i> < 0.05) were positively associated with initial trust, while no association was found between performance expectancy and initial trust (<i>p</i> > 0.05). Only social influence (β = 0.527, <i>p</i> < 0.05) was positively related to the behavioral intention. <b>Conclusions:</b> 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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