Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians
Open accessAbstract
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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Students generally expressed enthusiasm for integrating AI into their future medical practice, many believed AI had the potential to enhance and transform medicine by optimizing physicians' workloads and reducing healthcare costs, and students viewed AI as a supportive tool that can assist physicians, help mitigate medical errors, and enhance patient care.
“Overall, in addition to positive attitudes toward AI, students generally expressed enthusiasm for integrating it into their future medical practice [70, 71]. Many believed AI had the potential to enhance and transform medicine by optimizing physicians’ workloads and reducing healthcare costs [42, 72, 73]. Additionally, students viewed AI as a supportive tool that can assist physicians, help”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [71]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredSeveral 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 [16]
Tran et al identified social influence as the only significant predictor of the intention to use AI-CDSSs among undergraduate medical students.
“Tran et al [16] identified social influence as the only significant predictor of the intention to use AI-CDSSs among undergraduate medical students.”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [16]
The study found discrepancies in results compared with previous research findings.
“Finally, we focused on psychology students and psychotherapists in training as a potential user group and found discrepancies in our results compared with previous research findings [13,16].”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [16]
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“Overall, in addition to positive attitudes toward AI, students generally expressed enthusiasm for integrating it into their future medical practice [70, 71].”
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- Authors
- 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.
- Affiliations
- Hanoi Medical University (VN)
- Karolinska Institutet (SE)
- Duy Tan University (VN)
- Institute of Mental Health (SG)
- Trường ĐH Nguyễn Tất Thành (VN)
- Johns Hopkins University (US)
- National University of Singapore (SG)
- Corresponding author
- Vu, Linh Gia
- ORCID
- Venue
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Published
- 2021-11-26
- Volume
- 9
- Pages
- 755644-755644
- ISSN
- 2296-2565 (ISSN-L)
- PMC
- PMC8661093
- OpenAlex
- W3215718573
- Cited by
- 87
- References
- 58
- Citations by year
- 2026: 21 · 2025: 22 · 2024: 20 · 2023: 20 · 2022: 4
- FWCI
- 3.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- National University of Singapore (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National University of Singapore (R-177-000-003-001, R-722-000-004-731)
- 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
- artificial intelligence, diagnostic decision support, behavioral intention, medical students, UTAUT, social influence, effort expectancy, trust in AI, medical education, AI in clinical decision support
- MeSH terms
- Artificial Intelligence, Cross-Sectional Studies, Humans, Physicians, Prospective Studies, Intention
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