Perception-Decision-Execution Framework of Artificial Intelligence in Orthopedic Surgery: A Systematic Review from Theory to Clinical Practice

This article systematically reviews the application evolution of artificial intelligence in the field of orthopedic surgery, focusing on the three core links of perception, decision-making, and execution. It analyzes the technological development path from traditional rule-based methods to deep learning, then to multimodal fusion and closed-loop control, and discusses the clinical value and future challenges of key application scenarios such as personalized surgical planning, real-time navigation, and risk prediction.

Orthopedic surgery has long faced challenges such as high requirements for surgical precision, large individual differences, and long post-operative recovery cycles. With the aggravation of population aging and the increase in sports injuries, there is an urgent need for more efficient and precise orthopedic treatment methods. AI technology has shown potential in medical image analysis and disease diagnosis, but orthopedic surgery requires precise spatial operations and real-time decision-making. The core problem is how to effectively integrate AI into the entire orthopedic surgery process to form a complete closed loop from pre-operative planning to post-operative evaluation.

Perception is the foundation of intelligent surgical systems, with the core task of extracting clinical information from multi-source heterogeneous data (mainly medical image analysis). Early methods relied on traditional computer vision approaches (rule-based segmentation, handcrafted features) with limited generalization ability; after the introduction of CNNs, they achieved the level of professional physicians in tasks such as bone segmentation and lesion detection without manual feature design; the self-attention mechanism of the Transformer architecture improves the ability to understand complex anatomical structures, and multimodal fusion (X-ray, CT, MRI, and intraoperative images) has become a trend.

The decision-making layer connects perception and execution to formulate optimal surgical strategies. Early approaches used rule-based reasoning (encoding clinical guidelines and expert experience), which had high transparency but struggled to handle complex situations; data-driven methods (ensemble learning such as random forests) learn decision patterns from historical cases and perform well in tasks like risk assessment; deep learning integrates high-dimensional multi-type data (images, biochemical indicators, medical history, etc.) to form personalized recommendations, and reinforcement learning can optimize strategies in simulated environments.

The execution layer directly participates in surgical operations and is closely related to robotic technology. Early surgical robots passively executed according to preset trajectories, with limited applications; the new generation of systems has closed-loop control capabilities, integrating intraoperative images, force feedback, and real-time tracking data to dynamically adjust strategies; the trend is evolving from rigid operations to flexible intelligence, integrating multi-degree-of-freedom robotic arms, intelligent tools, and real-time navigation to adapt to complex scenarios such as soft tissue processing.

1. Personalized surgical planning: AI-based automatic generation of optimal plans (implant selection, path optimization, etc.), combined with 3D reconstruction and virtual reality to achieve pre-operative immersive simulation;
2. Real-time navigation and augmented reality: integrating optical tracking, electromagnetic positioning, and intraoperative images, AR technology overlays virtual information to enhance intuitiveness;
3. Risk prediction and safety monitoring: real-time analysis of intraoperative data streams to warn of abnormalities (such as nerve damage in spinal surgery), and post-operative complication prediction to guide preventive interventions.

Challenges:

1. Data quality and standardization (uneven data quality, high annotation costs, format differences);
2. Interpretability and trust building (contradiction between the black-box nature of deep learning and the need for medical transparency);
3. Regulatory and ethical frameworks (strict approval to balance innovation and safety);
4. Human-machine collaboration model (designing interaction interfaces and responsibility boundaries). Future: Multimodal large models, edge computing, 5G and other technologies will promote higher autonomy, and AI will evolve from an auxiliary tool to an intelligent partner.