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

J Med Syst. 2026 May 9;50(1):75. doi: 10.1007/s10916-026-02406-0.

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.

PMID:42105128 | DOI:10.1007/s10916-026-02406-0