SynthesisRheumatology international2026
Cost-effectiveness of artificial intelligence interventions for musculoskeletal disorders of the spine: a systematic review.
Synthesis in Rheumatology international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
Who cites it
0 citing papers in PubMed.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Musculoskeletal disorders (MSDs) represent a substantial global health burden, and artificial intelligence (AI)-based technologies are emerging as potential approaches for their management. However, evidence regarding the cost-effectiveness of these interventions remains limited. This systematic review synthesised the available evidence on the cost-effectiveness of AI-based technologies for managing MSDs of the spine. This systematic review was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. PubMed, Medline, CINAHL, Cochrane Central Register of Controlled Trials, Scopus, Web of Science, and Directory of Open Access Journals (DOAJ) databases were searched. After title and abstract screening, the remaining articles underwent full-text review by two independent reviewers, who extracted economic outcome data comparing AI-based interventions versus control. A total of 347 studies were identified, with 59 duplicates removed and 251 excluded based on title and abstract. After full-text review, seven eligible studies were included in the review. These studies represented five countries (United States (n = 2), Germany (n = 2), Denmark (n = 1), Australia (n = 1) and South Korea (n = 1)). The studies adopted a healthcare perspective (n = 4), a societal perspective (n = 2) and a healthcare and societal perspective (n = 1). Time durations varied widely, from four weeks to twelve months in trials to three years or lifetime in models, with one study using a 20-month post-implementation period. The AI components of the interventions encompassed a range of technologies, including a deep learning-based diagnostic and exercise prescription platform, AI-driven chest radiograph screening, the self-BACK application, standardised Magnetic Resonance Imaging (MRI) protocols, the Back Pain Choices online decision support tool, and the Kaia back pain application. In three studies, AI-based interventions for MSDs of the spine showed consistent positive QALY gains. Across the seven studies, five interventions (71%) were found to be cost-effective or cost-saving, four (57%) were dominant, while two (29%) were not cost-effective, indicating that most AI-based and digital interventions for MSDs of the spine provide favourable economic value compared with standard care. This is the first systematic review to synthesise evidence on the cost-effectiveness of AI-based technologies for the management of MSDs of the spine. The results suggest that AI-based interventions have the potential to contribute to more efficient healthcare resource use and may support policy and decision-making. However, their impact on reducing the economic burden of MSDs of the spine and improving health outcomes is not yet conclusive, and further standardised economic evaluations are needed to strengthen the evidence base. PROSPERO registration number: https://www.crd.york.ac.uk/PROSPERO/view/CRD42025642775 .
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