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Related Experiment Video

Updated: Jun 4, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
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Published on: August 5, 2021

Evaluation of text- and image-based generative artificial intelligence for simulating impacted third molar

Masakazu Hamada1, Kyoko Nishiyama1, Sachi Ichiyama1

  • 1Department of Oral & Maxillofacial Oncology and Surgery, Graduate School of Dentistry, The University of Osaka, Osaka, Japan.

Journal of Stomatology, Oral and Maxillofacial Surgery
|June 2, 2026
PubMed
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Generative AI shows promise for text-based surgical simulation of impacted mandibular third molar extraction. However, image generation requires further refinement for clinical accuracy.

Area of Science:

  • Dental Surgery
  • Artificial Intelligence
  • Medical Simulation

Background:

  • Generative artificial intelligence (AI) presents new possibilities for procedural simulation in medicine.
  • The clinical accuracy of generative AI for simulating impacted mandibular third molar extraction is not yet established.

Purpose of the Study:

  • To evaluate the clinical accuracy and quality of generative AI-driven text and image simulations for impacted mandibular third molar extraction.

Main Methods:

  • Generative AI created text and image simulations of a horizontally impacted mandibular third molar extraction.
  • Six dental experts assessed simulation quality using the Global Quality Scale and Likert Scale.
  • Statistical analysis was performed using the Kruskal-Wallis test.
Keywords:
Artificial intelligenceChatbotOral surgerySimulationTooth extraction

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Main Results:

  • Initial panoramic images were realistic but had inaccurate tooth positioning; iterative prompt adjustments improved accuracy.
  • Intraoperative images showed no significant improvement in quality.
  • Text-based procedural guidance received high scores with consistent quality across steps.

Conclusions:

  • Generative AI can produce suitable text-based guidance for surgical simulation.
  • Image-based AI outputs showed inconsistent anatomical accuracy, limiting their current clinical reliability.
  • Specialized dental AI trained on diverse data is needed for improved diagnosis and treatment planning.