Lower back pain (LBP) is a leading cause of disability globally, characterized by multifactorial origins that complicate accurate diagnosis and effective treatment planning. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and radiomics, has shown promise for improving the reproducibility and quantitative assessment of spine neuroimaging. This narrative review synthesizes current evidence on AI-derived imaging biomarkers in magnetic resonance imaging (MRI) and computed tomography (CT), with emphasis on disc degeneration, spinal stenosis, endplate signal abnormalities, paraspinal muscle composition, vertebral fractures, and spinal alignment. AI-based reconstruction, segmentation, and classification methods may reduce reader variability and enable standardized quantification of imaging features. However, the current evidence base remains dominated by technical and retrospective validation studies, and high diagnostic performance should not be interpreted as proof of improved patient-centered outcomes. The present review distinguishes technical feasibility, diagnostic assistance, prognostic association, and clinical utility, and highlights the persistent efficacy-effectiveness gap in AI-based spine imaging. Although multimodal models integrating imaging, clinical, biomechanical, and patient-reported data may improve future risk stratification, clinical translation remains constrained by heterogeneous datasets, limited external validation, incomplete interpretability, and evolving regulatory frameworks. Prospective multicenter validation and outcome-linked evaluation are required before AI-derived imaging biomarkers can be considered established tools for routine LBP management.
Stefanou et al. (2026) studied this question.