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Dergiler / Journal of Basic and Clinical Health Sciences / 2026 / Volume: 10 Issue: 3

Photograph-Based Visual Classification of Gingival Recession Using Multimodal Large Language Models: A Comparative Study

Mehmet Gümüş Kanmaz(İZMİR TINAZTEPE ÜNİVERSİTESİ)0000-0002-9261-7854
Burcu Kanmaz(DOKUZ EYLUL UNIVERSITY)0000-0001-9100-8398
Sayfa
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Yayın Tarihi
2026-09-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Conclusion: Under standardized single-prompt and photograph-only conditions, the evaluated multimodal large language models showed limited and inconsistent agreement with the periodontist panel visual consensus classification.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Purpose: This study aimed to evaluate the photograph-based visual classification performance of contemporary multimodal large language models for gingival recession defects according to Miller’s classification.
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Temel Bulgular & Veri:Material and Methods: A dataset of 100 standardized intraoral photographs showing gingival recession was used.
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Sonuç & Katkı:Conclusion: Under standardized single-prompt and photograph-only conditions, the evaluated multimodal large language models showed limited and inconsistent agreement with the periodontist panel visual consensus classification.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Artificial Intelligence · Gingival Recession · Large Language Models

Abstract

Purpose: This study aimed to evaluate the photograph-based visual classification performance of contemporary multimodal large language models for gingival recession defects according to Miller’s classification. Material and Methods: A dataset of 100 standardized intraoral photographs showing gingival recession was used. Five periodontists independently classified each photograph according to Miller’s classification using only the visual information available in the image. In cases of disagreement, a panel discussion was performed to establish a final periodontist visual consensus classification, which served as the reference comparator. Then photographs were submitted once to ChatGPT 5, Gemini 2.5 Pro, Grok 3, and Copilot GPT-5. Model outputs were compared with the periodontist panel consensus classification using overall accuracy, class-wise precision, sensitivity/recall, F1-score, Cohen’s kappa, and weighted kappa. Results: The periodontist panel showed almost perfect inter-rater agreement (Fleiss’ κ = 0.885; bootstrap 95% CI: 0.827–0.934). Overall classification accuracy differed significantly among the evaluated models (p < 0.001). Gemini showed the highest accuracy, correctly classifying 49% of photographs, followed by ChatGPT 5 with 32%, Grok 3 with 26%, and Copilot GPT-5 with 24%. Conclusion: Under standardized single-prompt and photograph-only conditions, the evaluated multimodal large language models showed limited and inconsistent agreement with the periodontist panel visual consensus classification.

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Bu Makaleye Atıf Yap

Kanmaz, M. G., Kanmaz, B. (2026). Photograph-Based Visual Classification of Gingival Recession Using Multimodal Large Language Models: A Comparative Study. Journal of Basic and Clinical Health Sciences, Volume: 10 Issue: 3. https://doi.org/10.30621/jbachs.1976204

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Kaynakça (26)

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