Clinician-supervised multimodal AI orchestration in spine care: evidence, framework, and future directions

Abstract

Degenerative, traumatic, deformity-related, and neoplastic spinal disorders place a growing burden on patients and health systems. Artificial intelligence (AI) has shown value in selected spine-care tasks, including imaging analysis, surgical-planning support, navigation assistance, risk prediction, rehabilitation monitoring, and early translational research. Most applications, however, remain task-specific tools rather than integrated clinical systems. This review therefore follows the routine spine-care pathway and focuses on clinician-supervised multimodal AI orchestration systems. In such systems, a DeepSeek-style large language model would be only one component. The broader clinical orchestration system would also require data governance, validated specialist modules, retrieval, uncertainty estimation, safety filters, audit trails, and clinician oversight. We review evidence from admission and imaging assessment through preoperative planning, intraoperative support, postoperative monitoring, and rehabilitation follow-up. We distinguish direct spine-specific evidence from indirect technical analogies and future hypotheses. Multi-omics and drug-development studies are considered only as an outer-loop translational layer for mechanism generation, endotype discovery, biomarker development, and trial enrichment. Current evidence supports selected diagnostic, prognostic, rehabilitation-monitoring, and workflow-assistance tasks, but not a safe, end-to-end autonomous platform for spinal surgery. Near-term translation should prioritize external validation, prospective silent testing, calibration, evidence traceability, post-deployment surveillance, and explicit clinician control.