Artificial Intelligence in Planning for Spine Surgery
Iyad Ali, Yianni Bakaes, James MacLeod, T. Lee, Sia Cho, Wellington K. Hsu
Abstract
PURPOSE OF REVIEW: There has been an expanding role of artificial intelligence (AI) and machine learning (ML) in spine surgery, particularly in operative planning, intraoperative navigation, and postoperative management. With a focus on patient-specific surgical strategies, AI technologies offer new possibilities for improving surgical accuracy, reducing risks, and enhancing patient outcomes in spine care. RECENT FINDINGS: AI models have shown strong accuracy in preoperative planning, with neural networks outperforming traditional algorithms in patient selection and outcome prediction. Advances in 3D modeling, supported by machine learning, enable efficient, patient-specific anatomical reconstructions, reducing manual segmentation time from hours to seconds. In intraoperative navigation, AI-driven virtual and augmented reality systems enhance screw placement precision and reduce radiation exposure by up to 90%, improving workflow and safety. Additionally, real-time AI-based decision support has decreased operative time and postoperative risks, while postoperative AI applications now support mortality risk stratification and discharge planning, yielding significant predictive accuracy for adverse events and extended stays. AI technologies are transforming spine surgery by increasing surgical precision, optimizing clinical workflows, and personalizing patient care. While challenges remain regarding data diversity and ethical considerations, ongoing innovations indicate that AI will continue to refine spine surgery through personalized and efficient care solutions.