{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T19:54:22Z","timestamp":1789761262289,"version":"4.0.1"},"reference-count":174,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>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\u2014including 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.<\/jats:p>","DOI":"10.3390\/bioengineering12090967","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T12:04:55Z","timestamp":1757505895000},"page":"967","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8574-2895","authenticated-orcid":false,"given":"Rahul","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, 1600 NW 10th Ave, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Conor","family":"Dougherty","sequence":"additional","affiliation":[{"name":"Sidney Kimmel Medical College, Thomas Jefferson University, 1025 Walnut St., Philadelphia, PA 19107, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5707-9009","authenticated-orcid":false,"given":"Kyle","family":"Sporn","sequence":"additional","affiliation":[{"name":"Norton College of Medicine, Upstate Medical University, Syracuse, NY 13210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4384-2693","authenticated-orcid":false,"given":"Akshay","family":"Khanna","sequence":"additional","affiliation":[{"name":"Sidney Kimmel Medical College, Thomas Jefferson University, 1025 Walnut St., Philadelphia, PA 19107, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Puja","family":"Ravi","sequence":"additional","affiliation":[{"name":"Department of Biology, University of Michigan, 500 S State St., Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1374-1900","authenticated-orcid":false,"given":"Pranay","family":"Prabhakar","sequence":"additional","affiliation":[{"name":"Albany Medical College, 43 New Scotland Ave, Albany, NY 12208, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0120-0939","authenticated-orcid":false,"given":"Nasif","family":"Zaman","sequence":"additional","affiliation":[{"name":"Human-Machine Perception Laboratory, Department of Computer Science, University of Nevada Reno, 1664 N. Virginia St. LME 314, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.3174\/ajnr.A7179","article-title":"Diagnostic Accuracy and Failure Mode Analysis of a Deep-Learning Algorithm for the Detection of Cervical Spine Fractures","volume":"42","author":"Voter","year":"2021","journal-title":"AJNR. Am. J. 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