The Impact of Language Variability on Artificial Intelligence Performance in Regenerative Endodontics

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dc.authorid0000-0003-0607-6703
dc.authorid0000-0003-4419-1518
dc.authorid0000-0002-0840-7126
dc.contributor.authorBuyukozer Ozkan, Hatice
dc.contributor.authorDogan Cankaya, Tulin
dc.contributor.authorKolus, Turkay
dc.date.accessioned2026-01-24T12:26:35Z
dc.date.available2026-01-24T12:26:35Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractBackground: Regenerative endodontic procedures (REPs) are promising treatments for immature teeth with necrotic pulp. Artificial intelligence (AI) is increasingly used in dentistry; thus, this study evaluates the reliability of AI-generated information on REPs, comparing four AI models against clinical guidelines. Methods: ChatGPT-4o, Claude 3.5 Sonnet, Grok 2, and Gemini 2.0 Advanced were tested with 20 REP-related questions from the ESE/AAE guidelines and expert consensus. Questions were posed in Turkish and English, with or without prompts. Two specialists assessed 640 AI-generated answers via a four-point rubric. Inter-rater reliability and response accuracy were statistically analyzed. Results: Inter-rater reliability was high (0.85-0.97). ChatGPT-4o showed higher accuracy with English prompts (p < 0.05). Claude was more accurate than Grok in the Turkish (nonprompted) and English (prompted) conditions (p < 0.05). No model reached >= 80% accuracy. Claude (English, prompted) scored highest; Grok-Turkish (nonprompted) scored lowest. Conclusions: The performance of AI models varies significantly across languages. English queries yield higher accuracy. While AI shows potential for REPs information, current models lack sufficient accuracy for clinical reliance. Cautious interpretation and validation against guidelines are essential. Further research is needed to enhance AI performance in specialized dental fields.
dc.identifier.doi10.3390/healthcare13101190
dc.identifier.issn2227-9032
dc.identifier.issue10
dc.identifier.pmid40428026
dc.identifier.scopus2-s2.0-105006432614
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/healthcare13101190
dc.identifier.urihttps://hdl.handle.net/20.500.12868/4765
dc.identifier.volume13
dc.identifier.wosWOS:001496290400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofHealthcare
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectartificial intelligence
dc.subjectregenerative endodontic procedures
dc.subjectChatGPT
dc.subjectClaude
dc.subjectGrok
dc.subjectGemini
dc.subjectendodontics
dc.subjectdental education
dc.titleThe Impact of Language Variability on Artificial Intelligence Performance in Regenerative Endodontics
dc.typeArticle

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