Anıl Murat ÖZTÜRK, Cemre AYDIN, Onur SÜER, Erhan SESLİ, Ömer AKÇALI, Emin ALICI
Journal of Turkish Spinal Surgery - 2026;37(EK-1):49-59
Artificial intelligence (AI) and machine learning (ML) are driving a paradigm shift in spine surgery, augmenting surgical decision-making with data-driven insights. This review synthesizes the current landscape of AI applications across the surgical care continuum and evaluates its potential to enhance precision, personalization, and value. A narrative review was conducted through a critical analysis of contemporary literature, including original research, systematic reviews, and editorials from high-impact orthopaedic and spine surgery journals. Key themes were identified and organized to provide a coherent overview of AI's role in preoperative planning, intraoperative execution, and postoperative economics. AI demonstrates significant utility in automating spinal imaging analysis, with convolutional neural networks enabling rapid vertebral segmentation and accurate measurement of alignment parameters. Predictive ML models excel in forecasting individualized patient risks, with specific algorithms outperforming surgeons in predicting complications and long-term outcomes. Intraoperatively, AI-driven navigation and robotic systems achieve a pedicle screw placement accuracy exceeding 94% while reducing radiation exposure. Furthermore, AI applications are emerging in health economics, effectively predicting costs and automating administrative tasks. Despite this, various challenges continue to hinder progress, notably the black-box nature of algorithms, data bias, ethical dilemmas, and barriers to clinical adoption. The available evidence positions AI not as a proven superior alternative, but as a promising adjunct with proof-of-concept applications across the spine care continuum. AI serves as a powerful adjunctive tool in spine surgery, promising to enhance procedural precision, personalize patient care, and improve economic efficiency. While limitations regarding transparency, data diversity, and ethical frameworks must be addressed, the ongoing development of explainable AI and robust datasets indicates a transformative future for spinal surgical practice.
