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Bone Bulletin

Abstract

Introduction

The past five decades in spine surgery have seen innovations in pedicle screw fixation, image-guided navigation, and first-generation robotics; each change was confronted with skepticism for uprooting existing paradigms until pushback inevitably yielded progress. Artificial intelligence (AI) and machine learning (ML) feel different in kind. Beyond better instrumentation, they represent entirely new frameworks for processing clinical information at unprecedented scale and speed. A neural network trained on thousands of preoperative records can learn to detect interactions among imaging findings, laboratory indices, functional variables, and comorbidity burden that conventional statistical models are structurally unable to identify.¹˒² Simultaneously, robotic platforms outfitted with computer vision and real-time anatomical segmentation capabilities are ushering in new operative environments where pre-planned trajectories execute with sub-millimetric fidelity.³ These innovations come at a time when surgical volumes are growing and patient complexity is increasing, with low back pain alone representing among the biggest global burdens of disability-adjusted life years.⁴ This paper examines recent evidence suggesting that novel AI and robotics may enhance spine surgery through preoperative risk prediction, intraoperative AI-driven computational advancements, and telerobotic surgery, and closes with a discussion on the limitations that must be addressed before these technologies can take their full effect.

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