Understanding Prospective Physicians’ Intention to Use Artificial Intelligence in Their Future Medical Practice: Configurational Analysis
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Abstract
Background: Prospective physicians are expected to find artificial intelligence (AI) to be a key technology in their future practice. This transformative change has caught the attention of scientists, educators, and policy makers alike, with substantive efforts dedicated to the selection and delivery of AI topics and competencies in the medical curriculum. Less is known about the behavioral perspective or the necessary and sufficient preconditions for medical students’ intention to use AI in the first place. Objective: Our study focused on medical students’ knowledge, experience, attitude, and beliefs related to AI and aimed to understand whether they are necessary conditions and form sufficient configurations of conditions associated with behavioral intentions to use AI in their future medical practice. Methods: We administered a 2-staged questionnaire operationalizing the variables of interest (ie, knowledge, experience, attitude, and beliefs related to AI, as well as intention to use AI) and recorded 184 responses at t0 (February 2020, before the COVID-19 pandemic) and 138 responses at t1 (January 2021, during the COVID-19 pandemic). Following established guidelines, we applied necessary condition analysis and fuzzy-set qualitative comparative analysis to analyze the data. Results: Findings from the fuzzy-set qualitative comparative analysis show that the intention to use AI is only observed when students have a strong belief in the role of AI (individually necessary condition); certain AI profiles, that is, combinations of knowledge and experience, attitudes and beliefs, and academic level and gender, are always associated with high intentions to use AI (equifinal and sufficient configurations); and profiles associated with nonhigh intentions cannot be inferred from profiles associated with high intentions (causal asymmetry). Conclusions: Our work contributes to prior knowledge by showing that a strong belief in the role of AI in the future of medical professions is a necessary condition for behavioral intentions to use AI. Moreover, we suggest that the preparation of medical students should go beyond teaching AI competencies and that educators need to account for the different AI profiles associated with high or nonhigh intentions to adopt AI.
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A specific study examining students' willingness to integrate AI into their future medical practices reported that a strong belief in a future role of AI correlated with behavioral intentions to use AI.
“Finally, a specific study examining students’ willingness to integrate AI into their future medical practices reported that a strong belief in a future role of AI correlated with behavioral intentions to use AI [75].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [75]
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- Authors
- Wagner, G., Raymond, L., & Paré, G.
- Affiliations
- University of Bamberg (DE)
- Université du Québec à Trois-Rivières (CA)
- HEC Montréal (CA)
- Corresponding author
- Wagner, Gerit
- Venue
- JMIR Medical Education
- Publisher
- JMIR Publications Inc.
- Published
- 2023-03-22
- Volume
- 9
- Pages
- e45631
- ISSN
- 2369-3762 (ISSN-L)
- PMC
- PMC10131981
- OpenAlex
- W4321794533
- Cited by
- 27
- References
- 54
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 10 · 2023: 4
- FWCI
- 1.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence in healthcare, medical students, intention to use AI, attitudes toward AI, AI competencies, medical curriculum, artificial intelligence adoption, gender differences, academic level
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