{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T06:07:34Z","timestamp":1788847654623,"version":"build-2803163510"},"reference-count":19,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"\n Background<\/jats:title>\n Large language models (LLMs) are reshaping medical education.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To examine whether learning approach\u2014AI-assisted, textbook-based, or blended\u2014was associated with diagnostic accuracy for acute abdominal conditions among emergency interns.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n We reviewed 720 clinical decisions by 72 emergency interns at a tertiary center over 12\u202fmonths. Interns were classified into three groups: textbook-based (\n n<\/jats:italic>\n =\u202f27), AI-assisted (\n n<\/jats:italic>\n =\u202f21), and blended (\n n<\/jats:italic>\n =\u202f24). Propensity score matching with pair-stratified GEE addressed selection bias and clustering.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Diagnostic accuracy was 73.0% (textbook), 82.4% (AI-assisted), and 87.1% (blended) (\n p<\/jats:italic>\n &lt;\u202f0.001). Blended learning showed the largest advantage over textbook (adjusted OR\u202f=\u202f3.21, 95% CI: 1.87\u20135.51,\n p<\/jats:italic>\n &lt;\u202f0.001), followed by AI-assisted (adjusted OR\u202f=\u202f1.68, 95% CI: 0.99\u20132.85,\n p<\/jats:italic>\n =\u202f0.053). Matched analyses confirmed the benefit for blended learning (\n p<\/jats:italic>\n =\u202f0.011). Subgroup analyses suggested larger effects for severe cases (OR\u202f=\u202f4.92) and atypical presentations (OR\u202f=\u202f3.85), with an E-value of 5.88 supporting robustness against unmeasured confounding.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Blended learning combining AI tools with traditional resources showed the strongest association with diagnostic accuracy, suggesting that integrated rather than exclusive approaches may better support clinical reasoning in emergency training.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1914097","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T05:34:06Z","timestamp":1788845646000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-assisted vs. textbook-based vs. blended learning for acute abdomen diagnosis: a retrospective cohort study of emergency interns"],"prefix":"10.3389","volume":"14","author":[{"given":"Weidi","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Trauma Center and Emergency Surgery, First Affiliated Hospital of Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Trauma Center and Emergency Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/1749-799X-8-3","article-title":"WSES guidelines for management of intra-abdominal infections","volume":"8","author":"Sartelli","year":"2013","journal-title":"World J Emerg 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