Dr. Ferran Pellisé presenting: Adult spinal deformity (ASD) is a complex and heterogeneous disease that can severely impact patients’ lives. While surgical correction can achieve significant improvement of spinopelvic parameters and quality of life measures in adults with spinal deformity, there remains a high risk of complication associated with surgical approaches to adult deformity. Over the past decade, utilization of surgical correction for ASD has increased dramatically as deformity correction techniques have become more refined and widely adopted. Along with this increase in surgical utilization, there has been a massive undertaking by spine surgeons to develop more robust models to predict postoperative outcomes to mitigate the relatively high complication rates. A large part of this revolution within spine surgery has been the gradual adoption of predictive analytics harnessing artificial intelligence using machine learning algorithms. The development of predictive models to accurately prognosticate patient outcomes following ASD surgery represents a dramatic improvement. Individualized medicine in the field of ASD shall improve patient care, by empowering both patients and surgeons to more specifically tailor clinical decisions to address individual health profiles and needs.