The impact of language variability on artificial intelligence performance in regenerative endodontics

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dc.contributor.authorBüyüközer Özkan, Hatice
dc.contributor.authorDoğan Çankaya, Tülin
dc.contributor.authorKölüş, Türkay
dc.date.accessioned2025-08-01T11:23:03Z
dc.date.available2025-08-01T11:23:03Z
dc.date.issuedMay 2025
dc.departmentKMÜ, Ahmet Keleşoğlu Diş Hekimliği Fakültesi, Klinik Bilimler Bölümü
dc.description.abstractRegenerative 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.issn22279032
dc.identifier.issue10
dc.identifier.pmid40428026
dc.identifier.startpage1190
dc.identifier.urihttps://www.doi.org/10.3390/healthcare13101190
dc.identifier.urihttps://hdl.handle.net/11492/10898
dc.identifier.volume13
dc.identifier.wosWOS:001496290400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.institutionauthorKölüş, Türkay
dc.institutionauthoridKölüş, Türkay/0000-0002-0840-7126
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofHealthcare (Switzerland)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial İntelligence
dc.subjectChatgpt
dc.subjectClaude
dc.subjectDental Education
dc.subjectEndodontics
dc.subjectGemini
dc.subjectGrok
dc.subjectRegenerative Endodontic Procedures
dc.titleThe impact of language variability on artificial intelligence performance in regenerative endodontics
dc.typeArticle

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