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Artificial intelligence in spine care: A scoping review of treatment applications
Victoria A Bensel1,2, Anne Habeck3, Marcda Hilaire Brunot4
1Department of Biomedical Informatics and Data Science: 100 College St Fl 9, New Haven, CT 06510 United States.
Background:
Artificial intelligence (AI) is increasingly applied in healthcare to support decision-making, personalize treatment, and improve outcomes. In spine care, AI has been used for both operative and nonoperative interventions, including surgical planning, outcome prediction, and digital tools for chronic low back pain (cLBP). However, evidence remains fragmented and variable in quality, limiting its utility for clinicians and researchers. This review maps the current literature on AI in spinal disorder treatment and highlights gaps for future research.
Methods:
This scoping review followed Joanna Briggs Institute (JBI) and PRISMA-ScR guidelines. Ovid MEDLINE, AMED, Embase, Cochrane CENTRAL, Web of Science, and Scopus were searched from January 2019 to December 2024. Eligible studies were English-language, peer-reviewed, involved AI applied to treatment interventions in human participants, included a comparison group, and provided sufficient methodological detail. No geographic restrictions were applied. Studies were evaluated by AI technology, treatment modality, outcomes, and quality. Methodological quality was assessed using a 19-point scoring system covering study design, reporting clarity, data validation, and feature selection.
Results:
The search yielded 1,782 manuscripts; 16 met inclusion criteria. Of these, 3 originated from the United States, 8 were single-country studies, and 5 were international collaborations. Fourteen studies focused on nonoperative management of musculoskeletal pain, particularly cLBP, using chatbots, AI-driven exercise platforms, and decision-support systems. These demonstrated modest improvements in pain, disability, and quality of life, with high adherence and satisfaction. Two studies investigated operative applications, reporting favorable results. Methodological scores ranged from 8.5 to 17/19, with common limitations in data validation and feature selection.
Conclusions:
Current literature demonstrates AI applications in nonoperative management of cLBP and in operative contexts such as surgical planning and outcome prediction. Most studies addressed cLBP, with limited exploration of neck pain, highlighting an area for future investigation.
