The Role of Artificial Intelligence in Spine Surgery: Current Update on Mechanisms and Clinical Implementation

Abstract

Study Design: Narrative review of contemporary literature (2021-2025) regarding artificial intelligence (AI) applications in spinal surgery.Objectives: This study aimed to summarize and critically evaluate advancements in AI systems and their clinical use in spine surgery, including diagnostic imaging, intraoperative navigation, predictive modeling, and robotic platforms.Summary of Literature Review: AI technologies such as machine learning, deep learning, natural language processing, and computer vision are increasingly integrated into spine surgery.These approaches enhance diagnostic accuracy, support refined surgical planning, and strengthen individualized prognostication.Despite these benefits, significant challenges remain, including data bias, lack of standardization, limited model generalizability, and unresolved ethical responsibilities.Understanding both the strengths and constraints of AI systems is essential for responsible and equitable clinical adoption.Materials and Methods: A targeted narrative search was conducted using PubMed and Google Scholar for English-language literature published between January 2021 and May 2025.Search terms included "AI," "spine," and "spinal."Studies describing diagnostic, perioperative, or prognostic applications were included, and additional references were identified through citation analysis.Approximately 17 key papers were reviewed.Results: AI-driven image analysis demonstrates high accuracy in detecting spinal abnormalities, degenerative changes, and neoplasms.Intraoperative AI-assisted navigation, robotic systems, and augmented reality platforms improve surgical precision and operative efficiency.Predictive models support risk stratification and forecasting of postoperative outcomes.Despite these promising developments, real-world implementation continues to be limited by data heterogeneity, regulatory and validation barriers, and uncertainty surrounding cost-effectiveness, particularly in healthcare environments with restricted resources.Conclusions: AI is revolutionizing spinal surgery by enhancing accuracy, safety, and individualized care.Ongoing multicenter validation, ethical data governance, and cost-effectiveness analysis are essential for sustainable clinical integration.

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