ArticleJMIR medical education2026
Medical AI Use Intention Among Medical Students and Faculty and Its Associations With AI Literacy, Perceived Benefits, and Risks: Cross-Sectional Survey Study.
Article in JMIR medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: AI is being increasingly integrated into health care and medical education. Although AI literacy is considered an essential competency for future physicians and educators, limited evidence exists regarding how perceived benefits, perceived risks, and AI literacy jointly influence AI use intention among different learner groups. Objective: This study examined the factors associated with AI use intention among medical students and faculty members and investigated whether the relationships between AI literacy and AI use intention differed according to learner group. Methods: A cross-sectional self-report survey was conducted among 141 medical students and 94 faculty members (N=235) at a single South Korean medical school. Group-specific linear regression models were estimated for descriptive purposes. To directly test whether associations differed between groups, a pooled regression model included group-by-predictor interaction terms for perceived benefits, perceived risks, AI literacy, and AI use frequency. HC3 heteroscedasticity-robust SEs were used for primary inference, and ordinal logistic regression was conducted as a sensitivity analysis because the 5-point intention outcome showed a marked ceiling effect. Results: Perceived benefits were positively associated with AI use intention in both groups. In the pooled HC3-robust model, the group-by-AI literacy interaction was significant ( Conclusions: Perceived benefits were consistently associated with stronger AI use intention. The adjusted association between AI literacy and use intention differed between students and faculty, although the subgroup-specific student coefficient was sensitive to model specification. These findings provide exploratory evidence that learner context may shape the relationship between AI literacy and broad self-reported AI use intention; they do not establish causal effects or the effectiveness of specific curricular strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.