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<p>Generative Artificial Intelligence (GenAI) is transforming medical education, including in the field of orthodontics. This paper presents an overview of prominent GenAI models and orthodontic platforms, such as ChatGPT, DeepSeek, GANs, and Diffusion Models, CephGPT-4, iOrthoPredictor, offer a wide range of capabilities, from knowledge-based Q&amp;A and clinical diagnostics to image generation and treatment simulation. The integration of GenAI with established teaching strategies and learning theories were also introduced, with exmples exploring GenAI applications across four key domains of orthodontic education: knowledge dissemination, clinical practice, teaching outcome assessment, and medical research. GenAI demonstrates capabilities in generating personalized educational content, optimizing curriculum design, and enhancing learning efficiency, and facilitates case simulation, diagnostic assistance, and virtual training modules, thereby supporting the development of practical clinical skills. The technology further contributes to education through personalized performance assessments and feedback mechanisms that improve learning outcomes. In research area, GenAI aids in literature retrieval, data analysis, and academic writing. Despite these promising applications, limitations such as inaccurate information, ethical challenges, excessive dependence, academic misconduct and educational integrity also exist. Proposed solutions involve the integration of GenAI with validated medical resources, implementation of robust data security protocols, and the establishment of guidelines for responsible utilization in educational settings. While GenAI offers significant potential to advance orthodontic education, its effective and ethical implementation requires careful navigation of these limitations and challenges. The development of appropriate safeguards and best practice guidelines will be essential to maximize benefits while mitigating risks associated with this emerging technology.</p>

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Generative Artificial Intelligence-driven orthodontic education practices

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月29日

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  • Menghan Zhang,
  • Yuzhi Yang,
  • Yan lv,
  • Yanfang Yu,
  • Sihui Hu,
  • Ziyuan Yang,
  • Zhiwei Wang,
  • Mengjie Wu

摘要

Generative Artificial Intelligence (GenAI) is transforming medical education, including in the field of orthodontics. This paper presents an overview of prominent GenAI models and orthodontic platforms, such as ChatGPT, DeepSeek, GANs, and Diffusion Models, CephGPT-4, iOrthoPredictor, offer a wide range of capabilities, from knowledge-based Q&A and clinical diagnostics to image generation and treatment simulation. The integration of GenAI with established teaching strategies and learning theories were also introduced, with exmples exploring GenAI applications across four key domains of orthodontic education: knowledge dissemination, clinical practice, teaching outcome assessment, and medical research. GenAI demonstrates capabilities in generating personalized educational content, optimizing curriculum design, and enhancing learning efficiency, and facilitates case simulation, diagnostic assistance, and virtual training modules, thereby supporting the development of practical clinical skills. The technology further contributes to education through personalized performance assessments and feedback mechanisms that improve learning outcomes. In research area, GenAI aids in literature retrieval, data analysis, and academic writing. Despite these promising applications, limitations such as inaccurate information, ethical challenges, excessive dependence, academic misconduct and educational integrity also exist. Proposed solutions involve the integration of GenAI with validated medical resources, implementation of robust data security protocols, and the establishment of guidelines for responsible utilization in educational settings. While GenAI offers significant potential to advance orthodontic education, its effective and ethical implementation requires careful navigation of these limitations and challenges. The development of appropriate safeguards and best practice guidelines will be essential to maximize benefits while mitigating risks associated with this emerging technology.