Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions

Abstract

Artificial intelligence (AI) drives transformative changes in orthopedic surgery, steering it toward precision and personalization through intelligent applications in preoperative planning, intraoperative assistance, and postoperative rehabilitation/monitoring. Breakthroughs in deep learning, robotics, and multimodal data fusion have enabled AI to demonstrate significant advantages. Nonetheless, current applications face challenges such as limited real-time decision autonomy, fragmented medical data silos, standardization gaps restricting model generalization, and ethical/regulatory frameworks lagging behind technological advancements. Therefore, a critical analysis of the current status of AI and the acceleration of its clinical translation is urgently required. This study systematically reviews the core advancements, challenges, and future directions of AI in orthopedic surgery from technical, clinical, and ethical perspectives. It elaborates on the “perceptual-decisional-executional” intelligent closed loop formed by algorithmic innovation and hardware upgrades, summarizes AI applications across surgical continuum, analyzes ethical and regulatory challenges, and explores emerging trajectories. This review integrates the end-to-end applications of AI in orthopedics, illustrating its evolution. It introduces an “algorithm-hardware-ethics trinity” framework for technical translation, providing methodological guidance for interdisciplinary collaboration. Additionally, it evaluates the combined efficacy of diverse algorithms and devices through practical cases and details of future research frontiers, aiming to inform researchers of current landscapes and guide subsequent investigations.

Keywords

artificial intelligence / deep learning / multimodal data fusion / orthopedic surgery / robot-assisted surgery

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