Use of Generative Artificial Intelligence in the Management of Low Back Pain: a Scoping Review

Clinical Snapshot

Key Findings

• P Value: Not applicable for scoping review

• Effect Size: 17 studies (55%) focused on diagnosis and treatment support

• Primary Outcome: 31 studies identified using GenAI in LBP management across four main categories

• Nnt Or Sensitivity: Higher agreement noted with simpler tasks, detailed prompts, and newer GenAI models

• Confidence Interval: Not applicable for scoping review

Clinical Application

Limited by current technology constraints, need for human oversight, and lack of clinical validation Relevant to Australian healthcare given Macquarie University authorship; aligns with RACGP digital health initiatives but requires TGA consideration for clinical applications Healthcare professionals managing LBP patients, particularly in primary care and specialist settings

Abstract

BACKGROUND CONTEXT: Low back pain (LBP) is common and a major cause of disability globally. Generative artificial intelligence (GenAI) such as ChatGPT may have the potential to improve LBP, but it is unknown how GenAI is being used in clinical care settings. PURPOSE: This scoping review aimed to map and synthesise the existing literature on the use of GenAI in the management of LBP across all settings. STUDY DESIGN: This review followed the scoping review methodology of the Joanna Briggs Institute and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. METHODS: We searched the Cochrane Library, Medline, Scopus, Embase, Web of Science, CINAHL, and preprint platforms for studies published between January 2012 and July 2025, on the use of GenAI in the management of LBP across all healthcare settings. The included studies were categorised according to the primary purpose of GenAI implementation. RESULTS: A total of 31 studies were included. Seventeen studies evaluated GenAI as a potential support tool for diagnosis and treatment, including answering specific questions about LBP (n = 8), generating advice from clinical cases (n = 4), and generating advice from diagnostic imaging (n = 5). Eight studies examined the use of GenAI to provide educational advice for patients. Other uses included research support (n = 5) and clinical documentation support (n = 1). GenAI advice for the management of LBP demonstrated higher agreement when using simpler tasks, more detailed prompts, or newer generation models. Reported errors in GenAI advice included inconsistency with guidelines or clinicians' advice, hallucination, low accuracy for complex questions, sensitivity to prompt design, and challenges with interpretability. CONCLUSION: Thirty-one studies reported the use of GenAI in LBP management, mainly supporting diagnosis and treatment planning, patient education, research, and clinical documentation. Despite authors' claims about promising uses of GenAI, it typically requires further refinement or review by human end-users. Critically, no studies addressed end-user experience, or clinical effectiveness, underscoring the need for robust clinical evaluation beyond the lab-based environment.

References

1. 1.Tu, R., French, S., Searant, I., Chung, J. W., Hancock, M., Magrabi, F., & Downie, A. (2026). Use of Generative Artificial Intelligence in the Management of Low Back Pain: a Scoping Review. Journal of Medical Systems. https://doi.org/10.2196/20346