{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T19:02:31Z","timestamp":1790276551043,"version":"4.1.0"},"reference-count":25,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Educ"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n 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\u2013robust 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Perceived benefits were positively associated with AI use intention in both groups. In the pooled HC3-robust model, the group-by\u2013AI literacy interaction was significant (\n B<\/jats:italic>\n =0.361, 95% CI 0.083-0.638;\n P<\/jats:italic>\n =.01), whereas the interactions for perceived benefits, perceived risks, and AI use frequency were not significant. The group-by\u2013AI literacy interaction remained significant in ordinal logistic regression (odds ratio 2.48, 95% CI 1.24-4.98;\n P<\/jats:italic>\n =.01). In subgroup models, the negative adjusted AI literacy coefficient among students was model dependent, and the positive perceived risk coefficient was not robust to HC3 inference. Among faculty members, AI use frequency remained positively associated with use intention using both HC3 linear regression (\n B<\/jats:italic>\n =0.137, 95% CI 0.025-0.249;\n P<\/jats:italic>\n =.02) and ordinal logistic regression (\n P<\/jats:italic>\n =.02).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/105032","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T18:52:47Z","timestamp":1790275967000},"page":"e105032-e105032","source":"Crossref","is-referenced-by-count":0,"title":["Medical AI Use Intention Among Medical Students and Faculty and Its Associations With AI Literacy, Perceived Benefits, and Risks: Cross-Sectional Survey Study"],"prefix":"10.2196","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2676-002X","authenticated-orcid":false,"given":"Bomyee","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9998-0226","authenticated-orcid":false,"given":"Gwanwook","family":"Bang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3060-8933","authenticated-orcid":false,"given":"Su Jin","family":"Chae","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2023.1227091","article-title":"Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm","volume":"6","author":"Krishnan","journal-title":"Front Artif Intell"},{"issue":"1","key":"R2","doi-asserted-by":"publisher","first-page":"849","DOI":"10.5334\/pme.2233","article-title":"Not replaced, but reinvented: AI education pathways to prepare future physicians to lead healthcare transformation","volume":"14","author":"Mehta","journal-title":"Perspect Med Educ"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1273\/cbij.25.53","article-title":"The third revolution of medicine by big data and artificial intelligence","volume":"25","author":"Tanaka","journal-title":"Chem Bio Inform J"},{"issue":"2","key":"R4","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1111\/jems.12555","article-title":"Augmenting physicians with artificial intelligence to transform healthcare: challenges and opportunities","volume":"33","author":"Agarwal","journal-title":"J Econ Manag Strategy"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.1177\/20552076231186520","article-title":"Artificial intelligence in healthcare: complementing, not replacing, doctors and healthcare providers","volume":"9","author":"Sezgin","journal-title":"Digit Health"},{"key":"R6","unstructured":"WMA statement on artificial and augmented intelligence in medical care. 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