Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians
Anh Quynh Tran, Long 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 Ho, Cyrus S. H. Ho
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.