Cost-effectiveness of artificial intelligence interventions for musculoskeletal disorders of the spine: a systematic review

Clinical Snapshot

Key Findings

• P Value: Not applicable for systematic review

• Effect Size: 71% of interventions (5/7) demonstrated cost-effectiveness or cost-saving

• Primary Outcome: Cost-effectiveness of AI interventions for spinal musculoskeletal disorders

• Nnt Or Sensitivity: 57% of interventions (4/7) were economically dominant over standard care

• Confidence Interval: Not reported

Clinical Application

Variable implementation feasibility depending on healthcare system digital infrastructure and clinician training requirements One included Australian study enhances local relevance. Potential PBS implications for digital health interventions and alignment with TGA software as medical device regulations. RACGP digital health guidelines may need updating to incorporate AI-based spine care tools. Patients with spinal musculoskeletal disorders across primary and secondary care settings

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 .

References

1. 1.Gebrye, T., Mbada, C., Niyi-Odumosu, F., Fatoye, C., Odetunde, M., Hakimi, Z., Useh, U., & Fatoye, F. (2026). Cost-effectiveness of artificial intelligence interventions for musculoskeletal disorders of the spine: a systematic review. Rheumatology International. https://doi.org/10.1136/bmjopen-2024-086800