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Cited by 9Artificial Intelligence in Healthcare and EducationMedical Imaging and AnalysisScoliosis diagnosis and treatment

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The Application of Artificial Intelligence in Spine Surgery: A Scoping Review

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Shi, Liangyu, Wang, Hongfei, Shea, Graham Ka-Hon·2025·JAAOS: Global Research and Reviews
DOI 10.5435/jaaosglobal-d-24-00405PMC PMC11999406
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Abstract

Background: A comprehensive review on the application of artificial intelligence (AI) within spine surgery as a specialty remains lacking. Methods: This scoping review was conducted upon PubMed and EMBASE databases according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Our analysis focused on publications from January 1, 2020, to March 31, 2024, with a specific focus on AI in the field of spine surgery. Review articles and articles predominantly concerning secondary validation of algorithms, medical physics, electronic devices, biomechanics, preclinical, and with a lack of clinical emphasis were excluded. Results: One hundred five studies were included after our inclusion/exclusion criteria were applied. Most studies (n = 100) were conducted through supervised learning upon prelabeled data sets. Overall, 38 studies used conventional machine learning methods upon predefined features, whereas 67 used deep learning methods, predominantly for medical image analyses. Only 25.7% of studies (27/105) collected data from more than 1,000 patients for model development and validation. Data originated from only a single center in 72 studies. The most common application was prognostication (38/105), followed by diagnosis (35/105), medical image processing (29/105), and surgical assistance (3/105). Conclusion: The application of AI within the domain of spine surgery has significant potential to advance patient-specific diagnosis, management, and surgical execution.

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Figure 1 PRISMA flow diagram showing the scoping review process. PRISM = Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
Figure 1 PRISMA flow diagram showing the scoping review process. PRISM = Preferred Reporting Items for Systematic Reviews and Meta-Analyses.CC BY-NC-ND · PubMed Central
Figure 2 Visual representation showing country of origin of study corresponding author.
Figure 2 Visual representation showing country of origin of study corresponding author.CC BY-NC-ND · PubMed Central
Figure 3 Pie chart showingsummary on the type of data used.
Figure 3 Pie chart showingsummary on the type of data used.CC BY-NC-ND · PubMed Central
Figure 4 Horizontal bar chart showingpatient number recruited for study data set.
Figure 4 Horizontal bar chart showingpatient number recruited for study data set.CC BY-NC-ND · PubMed Central
Figure 5 Donut chart showingmode of application of artificial intelligence in spine surgery.
Figure 5 Donut chart showingmode of application of artificial intelligence in spine surgery.CC BY-NC-ND · PubMed Central

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Metadata

Authors
Shi, L., Wang, H., & Shea, G. K.-H.
Affiliations
University of Hong Kong (HK)
Corresponding author
Shea, Graham Ka-Hon
ORCID
Venue
JAAOS: Global Research and Reviews
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2025-04
Volume
9
Issue
4
ISSN
2474-7661 (ISSN-L)
DOI
10.5435/jaaosglobal-d-24-00405
PMC
PMC11999406
OpenAlex
W4409488697
Cited by
9
References
1
Citations by year
2026: 4 · 2025: 5
FWCI
0.46
Type
journal article
Language
en
Open access
Licence
Locations
  • JAAOS Global Research and Reviews (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
  • Health Sciences › Medicine › Surgery › Scoliosis diagnosis and treatment
Keywords
artificial intelligence, spinal surgery, deep learning, medical image analysis, outcome prediction, diagnosis, surgical assistance, supervised learning, model validation
MeSH terms
Machine Learning, Deep Learning, Artificial Intelligence, Humans, Spinal Diseases, Spine, Orthopedic Procedures

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