Victoria A. Bensel, Anne Habeck, Marcda Hilaire Brunot, Eleni-James Becton, Monika Ray, Alexandria L. Brackett, Anthony J. Lisi
Abstract
Artificial intelligence (AI) is increasingly used to enhance diagnostic accuracy, automate image interpretation, and support clinical decision-making. In the field of spine care, applications include MRI and CT-based detection of lumbar disc degeneration, spinal stenosis, vertebral fractures, and axial spondyloarthritis, as well as emerging symptom-based and multimodal diagnostic tools. However, evidence remains dispersed across modalities and conditions, and the quality and clinical readiness of AI systems vary.
Introduction
Spinal disorders are among the leading causes of disability worldwide, contributing substantially to pain, reduced quality of life, and healthcare expenditures [1–3]. Conditions such as low back pain, spinal stenosis, spondylolisthesis, ankylosing spondylitis, and vertebral fractures represent a diverse spectrum of pathologies that often present with overlapping symptoms [4]. Accurate diagnosis is critical, as treatment pathways vary widely depending on the underlying etiology, disease severity, and patient comorbidities [5].
Methods
This scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews and follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines.
Search strategy
A comprehensive search strategy was developed in collaboration with a medical librarian (AB) to identify relevant studies on the application of AI in spine diagnostics. The search covered literature published between January 1, 2019, and December 31, 2024. This time window was selected to capture the period of rapid expansion in clinical AI research following the widespread adoption of deep learning methods in medical imaging, which accelerated substantially from 2019 onward [31].
Results
A total of 1,485 manuscripts were identified through searches across six databases. After removing 31 duplicates, 1,454 studies were screened by title and abstract. Ultimately, 46 studies met all criteria and were included in the final review (Fig 1). Forty-six studies met inclusion criteria (Table 3), published between 2019 and 2025 and conducted predominantly in Asia (n = 24), followed by Europe (n = 14), North America (n = 3), South America (n = 1), and the Middle East (n = 2), with two multinational cohorts.
Discussion
This scoping review provides an overview of recent applications of artificial intelligence for the diagnosis of spinal disorders. Across 46 included studies, the majority reported on imaging-based models, most often applied to MRI, CT, or radiographs. A smaller number incorporated clinical data or combined clinical and imaging inputs. The emphasis on imaging aligns with broader trends in medical AI research, where computer vision techniques dominate because of the relative availability of structured image datasets
Conclusions
We present a comprehensive overview of the literature on the application of AI in the diagnosis of spinal conditions. Studies frequently reported AI system performance comparable to or supportive of human interpretation. However, methodological variability, reporting transparency limitations, and reliance on retrospective single-center datasets were common.
Citation: Bensel VA, Habeck A, Brunot MH, Becton E-J, Ray M, Brackett AL, et al. (2026) Artificial intelligence in spine care: A scoping review of diagnostic applications. PLoS One 21(7): e0352200. https://doi.org/10.1371/journal.pone.0352200
Editor: Rajakumar Anbazhagan, National Institute of Child Health and Human Development (NICHD), NIH, UNITED STATES OF AMERICA
Received: January 6, 2026; Accepted: June 5, 2026; Published: July 28, 2026
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Data Availability: All relevant data are within the manuscript and its Supporting information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
