Scopus
🔓 Açık Erişim
Customized GPT-4V(ision) for radiographic diagnosis: can large language model detect supernumerary teeth?
BMC Oral Health · Aralık 2025
Makale Bilgileri
DergiBMC Oral Health
Yayın TarihiAralık 2025
Cilt / Sayfa25
Scopus ID2-s2.0-105005594668
Erişim🔓 Açık Erişim
Özet
Background: With the growing capabilities of language models like ChatGPT to process text and images, this study evaluated their accuracy in detecting supernumerary teeth on periapical radiographs. A customized GPT-4V model (CGPT-4V) was also developed to assess whether domain-specific training could improve diagnostic performance compared to standard GPT-4V and GPT-4o models. Methods: One hundred eighty periapical radiographs (90 with and 90 without supernumerary teeth) were evaluated using GPT-4 V, GPT-4o, and a fine-tuned CGPT-4V model. Each image was assessed separately with the standardized prompt “Are there any supernumerary teeth in the radiograph above?” to avoid contextual bias. Three dental experts scored the responses using a three-point Likert scale for positive cases and a binary scale for negatives. Chi-square tests and ROC analysis were used to compare model performances (p < 0.05). Results: Among the three models, CGPT-4 V exhibited the highest accuracy, detecting supernumerary teeth correctly in 91% of cases, compared to 77% for GPT-4o and 63% for GPT-4V. The CGPT-4V model also demonstrated a significantly lower false positive rate (16%) than GPT-4V (42%). A statistically significant difference was found between CGPT-4V and GPT-4o (p < 0.001), while no significant difference was observed between GPT-4V and CGPT-4V or between GPT-4V and GPT-4o. Additionally, CGPT-4V successfully identified multiple supernumerary teeth in radiographs where present. Conclusions: These findings highlight the diagnostic potential of customized GPT models in dental radiology. Future research should focus on multicenter validation, seamless clinical integration, and cost-effectiveness to support real-world implementation.
Yazarlar (3)
1
Enes Mustafa Aşar
ORCID: 0000-0003-3432-8584
2
İrem İpek
ORCID: 0000-0002-3542-7122
3
Kübra Bi̇lge
ORCID: 0000-0002-4323-9316
Anahtar Kelimeler
Artificial intelligence
ChatGPT-4V
Periapical Radiography
Supernumerary Teeth
Kurumlar
Firat Üniversitesi
Elazig Turkey
Selçuk Üniversitesi
Selçuklu Turkey