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Background <p>Spinal surgery is a&#xa0;highly complex field increasingly shaped by digital innovation. Artificial intelligence (AI) and machine learning (ML) offer new opportunities to enhance diagnostics, surgical planning, intraoperative precision, and postoperative care.</p> Methods <p>A&#xa0;narrative review was conducted based on a&#xa0;selective literature search in PubMed, Scopus, and Google Scholar. Included were peer-reviewed studies from the past 15&#xa0;years focusing on clinically relevant AI applications across pre-, intra-, and postoperative phases of spinal surgery.</p> Results <p>In the preoperative setting, AI supports history taking, imaging analysis (e.g., automated segmentation, fracture detection, scoliosis measurement), and personalized risk stratification. Intraoperatively, AI facilitates navigation, 3D reconstruction, augmented reality (AR) integration, and robotic assistance. Postoperatively, ML models predict complications, readmissions, and reherniations and assist with telemonitoring. Many systems are still in validation stages.</p> Conclusion <p>Artificial intelligence has the potential to fundamentally transform spinal surgery through increased precision, efficiency, and individualized decision support. Successful clinical integration requires validated algorithms, interdisciplinary collaboration, and ethical frameworks.</p>

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Künstliche Intelligenz in der Wirbelsäulenchirurgie: aktuelle Anwendungen und zukünftige Perspektiven

  • 期刊论文

  • 发表日期：

    2026年1月21日

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  • Filip Milicevic,
  • Mohamad Agha Mahmoud,
  • Maher Ghandour,
  • Moh’d Yazan Khasawneh,
  • Amir R. Ghasemi,
  • Koroush Kabir,
  • Ümit Mert

摘要

Background

Spinal surgery is a highly complex field increasingly shaped by digital innovation. Artificial intelligence (AI) and machine learning (ML) offer new opportunities to enhance diagnostics, surgical planning, intraoperative precision, and postoperative care.

Methods

A narrative review was conducted based on a selective literature search in PubMed, Scopus, and Google Scholar. Included were peer-reviewed studies from the past 15 years focusing on clinically relevant AI applications across pre-, intra-, and postoperative phases of spinal surgery.

Results

In the preoperative setting, AI supports history taking, imaging analysis (e.g., automated segmentation, fracture detection, scoliosis measurement), and personalized risk stratification. Intraoperatively, AI facilitates navigation, 3D reconstruction, augmented reality (AR) integration, and robotic assistance. Postoperatively, ML models predict complications, readmissions, and reherniations and assist with telemonitoring. Many systems are still in validation stages.

Conclusion

Artificial intelligence has the potential to fundamentally transform spinal surgery through increased precision, efficiency, and individualized decision support. Successful clinical integration requires validated algorithms, interdisciplinary collaboration, and ethical frameworks.