Exploring Artificial Intelligence in Orthopedic Surgery: A Review of Perception, Decision, and Execution Systems

Artificial intelligence (AI) has become an indispensable tool in orthopedic surgery. It provides new methods to increase surgical precision, improve patient safety, and support personalized treatment plans. This review presents a comprehensive analysis of AI-assisted orthopedic surgery across three core domains. Based on 89 recent studies, this review organizes findings around a perception–decision–execution framework. It groups diverse AI applications into certain categories while highlighting the mutuality across domains. Perception systems have progressed from basic CNN-based segmentation models to advanced transformer architectures. They support multi-modal data fusion and enable uncertainty quantification. Decision systems have moved far beyond rigid rule-based methods and evolve into data-driven models that support surgical planning, accurate risk prediction and continuous outcome optimization. And execution systems have advanced from passive navigation tools to active robotic assistance systems with real-time adaptive capabilities. Beyond mapping technological advances, this review also identifies pivotal challenges that hinder clinical translation and concludes with a clear roadmap for future research, which marks closed-loop surgical assistance systems as the next key development direction. Building on these findings, this review illuminates the potential of AI-assisted orthopedic surgery and guides future research toward innovations that can be translated into clinical practice.