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Updated: May 23, 2026

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Accuracy of Orthodontic Malocclusion Detection Using Multiple AI Models: A Comparative Study.

Hillda Herawati1, Joko Kusnoto2, Indrayadi Gunardi3

  • 1Doctoral Program in Dental Sciences, Faculty of Dentistry, Universitas Trisakti, Jakarta, Indonesia.

Healthcare Informatics Research
|May 21, 2026
PubMed
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This summary is machine-generated.

This study found that current artificial intelligence (AI) models have limited accuracy in detecting orthodontic malocclusions from intraoral photos, with performance varying by feature. Task-specific AI models are needed for better orthodontic decision support.

Area of Science:

  • Orthodontics
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate detection of orthodontic malocclusions is crucial for effective treatment planning.
  • The application of artificial intelligence (AI) in analyzing medical images, including intraoral photographs, is an emerging field.
  • Evaluating the diagnostic capabilities of general-purpose AI models in orthodontics is essential.

Purpose of the Study:

  • To compare the accuracy of leading AI models (ChatGPT, Gemini, Claude, Copilot) in identifying orthodontic malocclusion features.
  • To assess the agreement between AI model assessments and expert orthodontist evaluations.
  • To determine the performance of AI in detecting specific malocclusion parameters from standardized intraoral photographs.

Main Methods:

Keywords:
Artificial IntelligenceDiagnosisMalocclusionOrthodonticsROC Curve

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  • A cross-sectional study analyzed five standardized intraoral views from 50 children (aged 9-12).
  • Eight malocclusion parameters were assessed: anterior crowding, diastema, overjet, overbite, molar/canine relationships, crossbite, and arch symmetry.
  • Diagnostic accuracy was measured using Cohen's kappa and AUC, comparing AI models against an orthodontist's diagnosis.

    • Main Results:

    • AI models showed poor to moderate agreement (Cohen's κ: -0.15 to 0.63) with orthodontist assessments.
    • Easily visible features like crowding and diastema had higher accuracy (AUC: 0.56-0.85).
    • Complex features requiring spatial analysis (overbite, crossbite, arch symmetry) had low agreement and near-random performance (AUC: 0.41-0.70).

    Conclusions:

    • Current multimodal AI models have limited, feature-dependent accuracy for orthodontic malocclusion detection.
    • General-purpose AI systems are not yet sufficient for reliable orthodontic decision support.
    • Development of specialized AI models trained on clinically annotated orthodontic datasets is necessary.