{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T12:08:41Z","timestamp":1788955721703,"version":"build-2803163510"},"reference-count":0,"publisher":"Ubiquity Press, Ltd.","issue":"1","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Perspect Med Educ"],"abstract":"<jats:p>Background: Expert-reviewed clinical cases generated by large language models (LLMs) may supplement case resources in medical education, but their short-term educational performance relative to real-case-derived teaching materials remains uncertain. We compared immediate post-training test performance after teaching with the two types of case materials and assessed non-inferiority against a prespecified margin.Methods: We conducted a prospective, parallel-group, randomized non-inferiority trial. Through the Wenjuanxing online platform, participants were randomized 1:1 to learn with either real-case-derived teaching cases compiled by clinicians and reviewed by experts or AI-generated clinical cases produced by Gemini 3.0 Pro from fully de-identified matched real cases and reviewed by three senior general surgery specialists with full-professor rank. The primary outcome was the total score on an independent 10-item immediate post-training test (0\u201310 points), with a prespecified non-inferiority margin of \u20130.5 points. Secondary outcomes included the training-phase performance score, learning efficiency index, single-item mental effort rating, case realism, and case-source judgment.Results: A total of 403 participants were randomized, of whom 386 were included in the modified intention-to-treat analysis: 192 in the real-case group and 194 in the AI-generated case group. The mean post-training test score was 4.95 (SD, 3.35) in the real-case group and 4.61 (SD, 3.35) in the AI-generated case group. The mean difference (AI-generated minus real-case group) was \u20130.335 points (95% CI, \u20131.006 to 0.337). Because the lower bound of the confidence interval was below the prespecified non-inferiority margin of \u20130.5 points, non-inferiority was not demonstrated (one-sided P = 0.314). No significant between-group differences were observed in the training-phase performance score, learning efficiency index, or single-item mental effort rating. AI-generated cases received lower realism ratings for Level 3 cases. The proportion of participants with at least one high-confidence completely incorrect response was 1.6% in the real-case group and 2.1% in the AI-generated case group.Conclusions: In this short-term, text-based online case-learning setting, no statistically significant between-group difference was observed in immediate post-training test performance; however, non-inferiority of AI-generated clinical cases relative to real-case-derived teaching materials was not demonstrated.<\/jats:p>","DOI":"10.5334\/pme.2535","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:45:17Z","timestamp":1788954317000},"page":"759-770","source":"Crossref","is-referenced-by-count":0,"title":["Pedagogical Efficacy of LLM-Generated Synthetic Data Versus Real-World Clinical Records: A Randomized Controlled Non-Inferiority Trial"],"prefix":"10.5334","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7028-8992","authenticated-orcid":false,"given":"Zhongkun","family":"Zuo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0060-5859","authenticated-orcid":false,"given":"Jianyu","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7056-7297","authenticated-orcid":false,"given":"Muyao","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9107-726X","authenticated-orcid":false,"given":"Zedong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3285","published-online":{"date-parts":[[2026,9,9]]},"container-title":["Perspectives on Medical Education"],"original-title":[],"link":[{"URL":"https:\/\/pmejournal.org\/articles\/2535\/files\/6aa135535028b.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/pmejournal.org\/articles\/2535\/files\/6aa135b6ed839.xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/pmejournal.org\/articles\/2535\/files\/6aa1361d6f830.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/pmejournal.org\/articles\/2535\/files\/6aa135535028b.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:45:17Z","timestamp":1788954317000},"score":1,"resource":{"primary":{"URL":"https:\/\/pmejournal.org\/articles\/10.5334\/pme.2535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1,20]]}},"URL":"https:\/\/doi.org\/10.5334\/pme.2535","relation":{},"ISSN":["2212-277X"],"issn-type":[{"value":"2212-277X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]}}}