Advent of Artificial Intelligence in Spine Research: An Updated Perspective

Area of Science:

• Spine research and medical artificial intelligence (AI).

Background:

• AI has evolved from an experimental tool to a multi-domain framework impacting spine imaging analysis, surgical decisions, and outcome prediction.
• Recent AI advancements enable automated image interpretation, risk stratification, phenotype discovery, and data integration for spine care.

Purpose of the Study:

• To synthesize post-2019 AI advances in spine research across key domains.
• To evaluate these advances through a clinical-readiness lens, focusing on context, validation, and interpretability.
• To highlight AI's potential and identify steps for clinical integration in spine care.

Main Methods:

• Review and synthesis of recent (post-2019) artificial intelligence applications in spine research.
• Analysis across domains including imaging, predictive modeling, phenotyping, and language-based frameworks.
• Evaluation based on clinical context, validation rigor, and interpretability.

Main Results:

• AI shows significant progress in imaging analysis, predictive modeling, and phenotyping for spine care.
• Despite high internal performance, AI generalizability, interpretability, and clinical readiness remain limited.
• Challenges include dataset heterogeneity, transportability, and alignment with clinical workflows.

Conclusions:

• AI holds transformative potential for spine research and clinical practice.
• Addressing challenges in validation, generalizability, and interpretability is crucial for effective AI integration.
• Responsible and effective implementation requires careful consideration of clinical context and rigorous validation.