TY - JOUR T1 - Artificial Intelligence and Machine Learning Applications in Spine Surgery JF - International Journal of Spine Surgery JO - Int J Spine Surg DO - 10.14444/8503 SP - 8503 AU - Nathan J. Lee AU - Joseph M. Lombardi AU - Ronald A. Lehman Y1 - 2023/05/12 UR - http://ijssurgery.com//content/early/2023/05/11/8503.abstract N2 - The complexity of patients with spine pathology and high rates of complications has driven extensive research directed toward optimizing outcomes and reducing complications. Traditional statistical analysis has been limited both in validity and in the number of predictor variables considered. Over the past decade, artificial intelligence and machine learning have taken center stage as the possible solution to creating more accurate and applicable patient-centered predictive models in spine surgery. This review discusses the current published machine learning applications on preoperative optimization, risk stratification, and predictive modeling for the cervical, lumbar, and adult spinal deformity populations. ER -