Evaluation of Chat Generative Pretrained Transformer-5 and the Cameriere Method in Dental Age Estimation.

Introduction: Accurate age estimation is essential in medical and forensic practice. Dental development is among the most dependable biological indicators, and radiographic methods such as the Cameriere method have been validated across populations. Recently, vision-enabled large language models, in...

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Publicado en:Lokman Hekim Health Sciences Vol. 6; no. 2; pp. 203 - 212
Autores principales: Cömert, Hamide, Özdemir, Merve, Ataç, Atilla Stephan
Formato: diagnostic images research tables/charts Journal Article
Publicado: KARE Publishing Jun2026
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jun2026
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        atl: Evaluation of Chat Generative Pretrained Transformer-5 and the Cameriere Method in Dental Age Estimation.
      aug:
        au:
          Cömert, Hamide
          Özdemir, Merve
          Ataç, Atilla Stephan
        affil: Department of Pediatric Dentistry, Faculty of Dentistry, Lokman Hekim University, Ankara, Türkiye
      sug:
        subj:
          Artificial Intelligence, Generative Evaluation
          Age Determination by Teeth Methods
          Tooth Root Anatomy and Histology
          Human
          Male
          Female
          Child, Preschool
          Child
          Adolescence
          Retrospective Design
          Record Review
          Comparative Studies
          Methodological Research
          Radiography, Panoramic Methods
          Mandible Radiography
          Turkiye
          Software
          Dentists Psychosocial Factors
          Age Factors
          Sex Factors
          Paired T-Tests
          Wilcoxon Rank Sum Test
          Reliability
          Intraclass Correlation Coefficient
          Image Processing, Computer Assisted
          Dental Pulp Cavity Anatomy and Histology
          Descriptive Research
          Descriptive Statistics
          Data Analysis Software
          Child, Preschool: 2-5 years
          Child: 6-12 years
          Adolescent: 13-18 years
          Male
          Female
      ab: Introduction: Accurate age estimation is essential in medical and forensic practice. Dental development is among the most dependable biological indicators, and radiographic methods such as the Cameriere method have been validated across populations. Recently, vision-enabled large language models, including Chat Generative Pretrained Transformer-5 (ChatGPT-5), have attracted attention for image analysis. This study evaluated the performance of ChatGPT-5 in dental age (DA) estimation and compared its agreement with chronological age (CA) with that of the Cameriere method. Methods: This retrospective, comparative, methodological study analyzed 116 cropped panoramic radiographs of the mandibular left region from Turkish children aged 4.0-13.99 years. DA was estimated digitally using ImageJ software by two calibrated pediatric dentists applying the Cameriere method, and by ChatGPT-5 under two standardized prompting conditions (unguided and Cameriere-guided). Analyses were performed on the overall sample without sex-specific or age-specific subgroup evaluations. Agreement with CA was assessed using mean absolute error (MAE) and root mean square error (RMSE). Paired comparisons were conducted using paired t-tests or Wilcoxon signed-rank tests, depending on data distribution. Reliability was evaluated using intraclass correlation coefficients (ICC). Results: The Cameriere method demonstrated the highest accuracy and reliability (MAE=0.63 years; RMSE=0.81 years). ChatGPT-5 produced estimates that have greater variation. Performance improved when guided by the Cameriere formula, but reliability remained moderate (ICC=0.57). Discussion and Conclusion: While the Cameriere method provided more consistent age estimations, ChatGPT-5's estimates were more variable and insufficiently precise for clinical or forensic use.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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