Evidence map›Paper›PMID 42105128›Full record

ArticleJournal of medical systems2026

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

Renjie Tu, Simon French, Isaac Searant, Jae Woo Chung, Mark Hancock, Farah Magrabi, Aron Downie

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Renjie TuDepartment of Chiropractic and Spinal Pain Research Centre, Macquarie University, Sydney, NSW, Australia. renjie.tu@mq.edu.au.
Simon FrenchDepartment of Chiropractic and Spinal Pain Research Centre, Macquarie University, Sydney, NSW, Australia.
Isaac SearantDepartment of Chiropractic and Spinal Pain Research Centre, Macquarie University, Sydney, NSW, Australia.
Jae Woo ChungDepartment of Chiropractic and Spinal Pain Research Centre, Macquarie University, Sydney, NSW, Australia.
Mark HancockDepartment of Health Sciences and Spinal Pain Research Centre, Macquarie University, Sydney, NSW, Australia.
Farah MagrabiCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Macquarie Park, NSW, Australia.
Aron DownieDepartment of Chiropractic and Spinal Pain Research Centre, Macquarie University, Sydney, NSW, Australia. aron.downie@mq.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background contextLow 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

designThis 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.

methodsWe 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.

resultsA 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.

conclusionThirty-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.

Indexed as

Generative Artificial IntelligenceLow Back PainHumansChatGPTDecision support systemsGenerative artificial intelligenceLow back painPatient educationScoping review

Identifiers

PMID42105128
PMCPMC13157360

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.