Artificial intelligence (AI) has rapidly evolved from an experimental tool in spine research to a multi-domain framework that has significantly influenced imaging analysis, surgical decision-making, and individualized outcome prediction. Recent advances have expanded beyond isolated applications, enabling automated image interpretation, patient-specific risk stratification, discovery of qualitative phenotypes, and integration of heterogeneous clinical and biomechanical data. These developments signal a shift toward more comprehensive, context-aware analytic systems capable of supporting complex clinical workflows in spine care. Despite these gains, widespread clinical adoption remains limited. High internal performance metrics do not consistently translate into reliable generalizability, interpretability, or real-world clinical readiness. Persistent challenges, which include dataset heterogeneity, transportability across institutions, alignment with clinical decision-making processes, and appropriate validation strategies, continue to constrain widespread implementation. In this perspective, we synthesize post-2019 advances in spine AI across key application domains: imaging analysis, predictive modeling and decision support, qualitative phenotyping, and emerging hybrid and language-based frameworks through a unified clinical-readiness lens. By examining how methodological progress aligns with clinical context, validation rigor, and interpretability, we highlight both the transformative potential of AI in spine research and the critical steps required for responsible, effective integration into routine clinical practice.
Maity et al. (2026) studied this question.