Analysis of Accuracy and Response Stability of Artificial Intelligence Models in Answering Diagnostic, Therapeutic and Prognostic Questions on Dental Trauma
Australian Endodontic Journal, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1111/aej.70103
- Dergi Adı: Australian Endodontic Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, MEDLINE, Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: artificial intelligence, dental trauma, diagnostic, prognostic, therapeutic
- Lokman Hekim Üniversitesi Adresli: Evet
Özet
This study comparatively evaluated accuracy and response stability of artificial intelligence (AI)-models in answering diagnostic, therapeutic and prognostic questions related to dental trauma (DT), based on International Association of Dental Traumatology (IADT) guidelines. Fifty multiple-choice questions derived from IADT guidelines were categorised into diagnostic (n = 13), therapeutic (n = 30) and prognostic (n = 7) domains and administered to eight AI-models once weekly over three consecutive weeks. Responses were coded as correct (1) or incorrect (0) and analysed. Statistical significance was set at p < 0.05. Most models showed significant response stability, except ChatGPT-4.5 (κ = −0.007, p = 0.934). ChatGPT-5 showed moderate agreement (κ = 0.479, p < 0.05). Accuracy differed among models for therapeutic and overall questions (p < 0.05), but not for diagnostic or prognostic domains (p > 0.05). Model type significantly affected accuracy (p = 0.001), whereas question category (p = 0.259) and time (p = 0.436) had no effect. AI models showed heterogeneous performance. High accuracy did not necessarily correspond to response stability, as observed for ChatGPT-4.5, indicating that these systems should be used cautiously and only as supplementary tools within a structured multiple-choice framework.