Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.
Publication/Presentation Date
9-9-2025
Abstract
The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain-including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery.
Volume
12
Issue
9
ISSN
2306-5354
Published In/Presented At
Kumar, R., Dougherty, C., Sporn, K., Khanna, A., Ravi, P., Prabhakar, P., & Zaman, N. (2025). Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care. Bioengineering (Basel, Switzerland), 12(9), 967. https://doi.org/10.3390/bioengineering12090967
Disciplines
Education | Medical Education
PubMedID
41007212
Department(s)
Department of Education
Document Type
Article