Machine Learning in Spine Surgery: A Narrative Review
Samuel Adida, Aimé Parra Legarreta, Joseph S. Hudson, David McCarthy, Edward Andrews, Regan M. Shanahan, Suchet Taori, Raj Swaroop Lavadi, Thomas J. Buell, D. Kojo Hamilton, Nitin Agarwal, Peter C. Gerszten
Abstract
Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.