{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T22:11:33Z","timestamp":1791929493212,"version":"4.3.4"},"reference-count":44,"publisher":"Wiley","license":[{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int Endodontic J"],"abstract":"ABSTRACT<\/jats:title>\n \n Aim<\/jats:title>\n \n To pioneer the first comprehensive benchmarking of dental students against 22 multimodal Artificial Intelligence (\n AI<\/jats:styled-content>\n ) models spanning six major architectures (Anthropic,\n DeepSeek<\/jats:styled-content>\n , Google Gemini,\n OpenAI<\/jats:styled-content>\n , Meta Llama, Mistral\/Qwen) in emergency permanent tooth avulsion management, quantifying performance differences across Bloom's Taxonomy higher\u2010order cognitive domains (Apply, Analyse, Evaluate) against International Association of Dental Traumatology (\n IADT<\/jats:styled-content>\n ) guidelines to optimise evidence\u2010based educational integration.\n <\/jats:p>\n <\/jats:sec>\n \n Methodology<\/jats:title>\n \n This cross\u2010sectional study compared 35 fifth\u2010year dental students with 22 multimodal\n AI<\/jats:styled-content>\n models grouped by producer (Anthropic,\n DeepSeek<\/jats:styled-content>\n , Google Gemini,\n OpenAI<\/jats:styled-content>\n , Meta, Mistral\/Qwen) using a validated clinical vignette featuring a 14\u2010year\u2010old with three avulsed mature permanent teeth (#11, 12, 22) with clinical photograph. Performance was assessed through four questions (1 Apply, 2 Analyse, 1 Evaluate) via an\n IADT<\/jats:styled-content>\n \u2010aligned rubric validated against paediatric dentistry standards. Responses were independently scored by two blinded evaluators. Subsequently, scores were compared using\n ANOVA<\/jats:styled-content>\n with post hoc Tukey\n HSD<\/jats:styled-content>\n , Kruskal\u2010Wallis, and chi\u2010square tests (\n SPSS<\/jats:styled-content>\n v20,\n \u03b1<\/jats:italic>\n \u2009=\u20090.05).\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Overall performance analysis revealed significant group differences (\n p<\/jats:italic>\n \u2009=\u20090.012, partial\n \u03b7<\/jats:italic>\n \n 2<\/jats:italic>\n <\/jats:sup>\n \u2009=\u20090.27). Only students achieved high\u2010acceptability A\u2010level performance (17.1%,\n \u03c7<\/jats:italic>\n 2<\/jats:sup>\n \u2009=\u200939.27,\n p<\/jats:italic>\n \u2009=\u20090.003), despite top\n AI<\/jats:styled-content>\n models (Claude\u2010Sonnet\u20103.7\u2010Reasoning,\n DeepSeek<\/jats:styled-content>\n \u2010\n R1<\/jats:styled-content>\n : 7.0\/10) marginally exceeding student mean scores (6.5\/10). Performance varied among\n AI<\/jats:styled-content>\n systems, with students significantly outperforming Meta Llama models (mean difference\u2009=\u20093.50,\n p<\/jats:italic>\n \u2009=\u20090.014), which showed exclusively not acceptable (D\u2010level) performance. Students dominated Bloom's Analyse level (visual diagnosis: 100% tooth identification vs. 77.3%\n AI<\/jats:styled-content>\n failure; age\u2010specific management differentiation,\n p<\/jats:italic>\n \u2009<\u20090.001), while\n AI<\/jats:styled-content>\n excelled in Apply\/Emergency steps (medical management: 81.8% perfect scores) and Evaluate\/prosthetic contraindication reasoning (Anthropic: 0.92\u2009\u00b1\u20090.20 vs. students: 0.41\u2009\u00b1\u20090.33,\n p<\/jats:italic>\n \u2009=\u20090.005). Critical\n AI<\/jats:styled-content>\n deficiencies included tooth misidentification (77% of models), failure to mention follow\u2010up schedules for immature replanted teeth (100%), and inappropriate intervention recommendations (40.9% suggested unsuitable options like resin\u2010bonded bridges). For soft tissue management, only 23% of\n AI<\/jats:styled-content>\n models addressed the need to suture gingival lacerations.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n \n AI<\/jats:styled-content>\n 's fundamental limitations in visual diagnosis,\n IADT<\/jats:styled-content>\n misinterpretations, and inadequate clinical translation mandate strict positioning as a supplemental protocol accelerator\u2014not an independent diagnostic tool. Educational integration requires: (1) Careful model selection and supervision for any visual triage applications; (2) Mandatory\n IADT<\/jats:styled-content>\n verification; and (3) Preservation of human clinical reasoning as the irreplaceable cornerstone of permanent tooth avulsion management.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1111\/iej.70060","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T12:04:16Z","timestamp":1762862656000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Emergency Management of Permanent Tooth Avulsion: Comparative Performance of Dental Students and Artificial Intelligence Using a Multimodal Clinical Vignette Across Bloom's Taxonomy Domains"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5210-5128","authenticated-orcid":false,"given":"Sanaa N.","family":"Al\u2010Haj Ali","sequence":"first","affiliation":[{"name":"Department of Orthodontics and Pediatric Dentistry, College of Dentistry Qassim University Qassim Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,11]]},"reference":[{"issue":"3","key":"e_1_2_11_2_1","doi-asserted-by":"crossref","first-page":"152","DOI":"10.3163\/1536-5050.103.3.010","article-title":"Bloom's Taxonomy of Cognitive Learning Objectives","volume":"103","author":"Adams N. 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