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<p>Artificial intelligence (AI) in orthopedics and trauma surgery is currently transitioning from a&#xa0;buzzword to actual clinical applications; however, the extent to which such applications are already taking place varies considerably depending on the specific domain. The use of AI-assisted systems for fracture detection and automated measurement of angles and other bone-referenced parameters on X‑radiographs now achieve accuracy comparable to that of experienced specialists. Corresponding applications have now received regulatory approval as medical devices and have already entered clinical practice. Predictive models for forecasting the length of stay, mobility at discharge and complication rates following orthopedic and trauma surgery procedures show considerable potential but are still undergoing external validation. Digital twins, computer-based patient-specific simulation models, offer promising possibilities for treatment planning and clinical decision-making but are still largely at the developmental stage in clinical settings. Fully autonomous surgical systems are likely to remain a&#xa0;distant prospect for the foreseeable future given the unresolved legal, ethical and technical hurdles. Consequently, a&#xa0;critical evidence-based engagement with AI technologies is an integral part of professional responsibility in orthopedics and trauma surgery.</p>

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Zwischen klinischer Realität und Zukunftsmusik

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月21日

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  • Lorenz Pichler,
  • Jennifer Straub,
  • Sebastian Simon,
  • Julius Watrinet

摘要

Artificial intelligence (AI) in orthopedics and trauma surgery is currently transitioning from a buzzword to actual clinical applications; however, the extent to which such applications are already taking place varies considerably depending on the specific domain. The use of AI-assisted systems for fracture detection and automated measurement of angles and other bone-referenced parameters on X‑radiographs now achieve accuracy comparable to that of experienced specialists. Corresponding applications have now received regulatory approval as medical devices and have already entered clinical practice. Predictive models for forecasting the length of stay, mobility at discharge and complication rates following orthopedic and trauma surgery procedures show considerable potential but are still undergoing external validation. Digital twins, computer-based patient-specific simulation models, offer promising possibilities for treatment planning and clinical decision-making but are still largely at the developmental stage in clinical settings. Fully autonomous surgical systems are likely to remain a distant prospect for the foreseeable future given the unresolved legal, ethical and technical hurdles. Consequently, a critical evidence-based engagement with AI technologies is an integral part of professional responsibility in orthopedics and trauma surgery.