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<p>In recent years, digital implant planning in dental implantology has undergone a&#xa0;significant transformation, with three-dimensional imaging technologies such as cone beam computed tomography (CBCT) and intraoral scanning (IOS) playing a&#xa0;central role. These technologies have not only improved diagnostics and treatment planning but have also replaced conventional methods in clinical practice. The next step in this evolution is the integration of artificial intelligence (AI), which assists implantologists in making more precise diagnoses, developing treatment plans, and predicting implant success. AI applications aid in the analysis of 3D imaging data, identification of anatomical structures, automated implant planning, and prediction of peri-implant complications. The efficiency and accuracy of these systems are increasingly being evaluated in clinical practice. Despite advancements, limitations remain, particularly regarding fully automated workflows and the validation of commercial software packages. The future of implantology will be shaped by the further development of AI-driven tools, enabling optimized treatment planning and improved implant prognosis.</p>

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Künstliche Intelligenz in der dentalen Implantologie

  • 期刊论文

  • 发表日期：

    2025年1月10日

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  • Maxim Van den Bempt,
  • Susanne Nahles,
  • Max Heiland,
  • Tabea Flügge

摘要

In recent years, digital implant planning in dental implantology has undergone a significant transformation, with three-dimensional imaging technologies such as cone beam computed tomography (CBCT) and intraoral scanning (IOS) playing a central role. These technologies have not only improved diagnostics and treatment planning but have also replaced conventional methods in clinical practice. The next step in this evolution is the integration of artificial intelligence (AI), which assists implantologists in making more precise diagnoses, developing treatment plans, and predicting implant success. AI applications aid in the analysis of 3D imaging data, identification of anatomical structures, automated implant planning, and prediction of peri-implant complications. The efficiency and accuracy of these systems are increasingly being evaluated in clinical practice. Despite advancements, limitations remain, particularly regarding fully automated workflows and the validation of commercial software packages. The future of implantology will be shaped by the further development of AI-driven tools, enabling optimized treatment planning and improved implant prognosis.