Artificial Intelligence Empowering Spinal Deformity Surgery: From Clinical Assessment to Decision Integration

The article systematically maps the application landscape of AI across core clinical links in spinal deformity surgery, covering imaging parameter measurement, surgical risk prediction, and individualized plan formulation. Compared with prior discussions that address a single link in isolation, this article integrates multiple key links into one chain from assessment to decision-making from a clinical-expert perspective. It is an expert-perspective review-style discussion, presented as a systematic exposition of application progress and value, without specific study data or sample information.

The article notes that the traditional "experience-driven" clinical decision-making model has limitations such as strong subjectivity and insufficient standardization, while AI, especially deep learning, brings opportunities in medical image analysis, risk prediction, and surgical planning. It directly connects the decision-making pain points of spinal deformity, a disease with complex anatomy and significant individual heterogeneity, to the capabilities of AI technology. This is a summative judgment based on the current state of the field and the characteristics of AI technology, and is an opinion-based argument.

The article proposes a "human-machine co-intelligence" direction, emphasizing that AI must be integrated with clinical experience to move the discipline from "standardized treatment" toward "individualized precision treatment" and to build an intelligent diagnosis and treatment system. It elevates AI from a mere tool to a decision-integration element that works alongside clinical experience, and offers a directional judgment on the discipline's transformation. This is a forward-looking outlook and conceptual proposition, with no empirical data in the text validating the effects of such a transformation.

The article analyzes the bottlenecks in promoting AI technology in spinal deformity surgery. Beyond presenting application value, it also brings the obstacles to technology promotion into the discussion, making the account closer to real-world implementation. The text only states that it "analyzes bottlenecks in technology promotion" and does not list specific bottleneck items at the abstract level.

The scope of this article is the clinical assessment and decision-integration setting of spinal deformity surgery, aimed at clinical experts and researchers interested in AI applications in orthopedic imaging measurement, surgical risk prediction, and individualized plan formulation; its conclusions are presented as an expert-perspective review and outlook, suitable as a framework reference for understanding the application landscape and the "human-machine co-intelligence" direction in this field.

Readers may still want to watch: what specific items the "bottlenecks in technology promotion" include, what research or data support the reported progress of AI in each core link, and how the "human-machine co-intelligence" and the transformation toward individualized precision treatment would be implemented and evaluated; because this is a fast abstract-level parse lacking figures and full-text details, these aspects cannot be expanded in this summary.