Public articles linked to the same research event.
Zhonghua yi xue za zhi From a clinical-expert perspective, this article reviews the progress and value of artificial intelligence, especially deep learning, in core links of spinal deformity surgery such as imaging parameter measurement, surgical risk prediction, and individualized plan formulation, analyzes bottlenecks in technology promotion, and looks ahead to a "human-machine co-intelligence" direction, arguing that integrating AI with clinical experience can move the discipline from "standardized treatment" toward "individualized precision treatment" and build an intelligent diagnosis and treatment system.
From a clinical-expert perspective, this article reviews the progress and value of artificial intelligence, especially deep learning, in core links of spinal deformity surgery such as imaging parameter measurement, surgical risk prediction, and individualized plan formulation, analyzes bottlenecks in technology promotion, and looks ahead to a "human-machine co-intelligence" direction, arguing that integrating AI with clinical experience can move the discipline from "standardized treatment" toward "individualized precision treatment" and build an intelligent diagnosis and treatment system.
From a clinical-expert perspective, this article reviews the progress and value of artificial intelligence, especially deep learning, in core links of spinal deformity surgery such as imaging parameter measurement, surgical risk prediction, and individualized plan formulation, analyzes bottlenecks in technology promotion, and looks ahead to a "human-machine co-intelligence" direction, arguing that integrating AI with clinical experience can move the discipline from "standardized treatment" toward "individualized precision treatment" and build an intelligent diagnosis and treatment system.
From a clinical-expert perspective, this article reviews the progress and value of artificial intelligence, especially deep learning, in core links of spinal deformity surgery such as imaging parameter measurement, surgical risk prediction, and individualized plan formulation, analyzes bottlenecks in technology promotion, and looks ahead to a "human-machine co-intelligence" direction, arguing that integrating AI with clinical experience can move the discipline from "standardized treatment" toward "individualized precision treatment" and build an intelligent diagnosis and treatment system.