Accepted for/Published in: JMIR Medical Education
Date Submitted: Jan 10, 2023
Open Peer Review Period: Jan 10, 2023 - Jan 30, 2023
Date Accepted: Feb 24, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Understanding prospective physicians’ intention to use artificial intelligence in their future medical practice: A configurational analysis
ABSTRACT
Background:
Prospective physicians are expected to find artificial intelligence (AI) a key technology in their future practice. This transformative change has caught the attention of scientists, educators, and policymakers 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 focuses on medical students’ knowledge, experience, attitude, and beliefs related to AI and aims 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 two-staged questionnaire operationalizing the variables of interest (i.e., knowledge, experience, attitude, and beliefs related to AI, as well as intention to use AI), and recorded 184 responses at (February 2020, before COVID-19) and 138 responses at (January 2021, during COVID-19). Following established guidelines, we applied necessary condition analysis and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the data.
Results:
Findings from the fsQCA show that (1) intention to use AI is only observed when students have a strong belief in the role of AI (individually necessary condition), (2) certain AI profiles, i.e., combinations of knowledge and experience, attitudes and beliefs, as well as academic level and gender, are always associated with high intentions to use AI (equifinal and sufficient configurations), and (3) profiles associated with non-high 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 non-high intentions to adopt AI.
Citation
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Copyright
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