AI-assisted physiotherapy for patients with non-specific low back pain: a systematic review and meta-analysis
File(s)
Published version
Author(s)
Kapil, Dev
Wang, Jin
Olawade, David B
Vanderbloemen, Laura
Type
Journal Article
Abstract
Background: Non-specific low back pain (LBP) is a widespread condition with significant impacts on physical activity, muscle strength, psychological well-being, and economic status. Traditional physiotherapy shows variable efficacy, prompting growing interest in AI-assisted physiotherapy for its potential to offer personalized feedback and multidisciplinary care integration. Objective: This systematic review and meta-analysis aimed to evaluate AI-assisted physiotherapy’s effectiveness in reducing pain intensity and functional impairment and improving mental health compared to usual physiotherapy. Method: A comprehensive search strategy was employed across Embase, MEDLINE, Cochrane Library, and Web of Science databases from inception to 30 May 2024. Comparative studies were identified and screened using PICOS criteria. Data extraction involved detailed study characteristics and outcomes, with methodological quality assessed via the Cochrane Risk of Bias tool. Meta-analyses using random-effects models calculated standardized mean differences (SMDs). Results: Eight studies met the inclusion criteria. Compared to usual physiotherapy, AI-assisted physiotherapy did not demonstrate any statistically significant differences in outcomes across the aspects studied, including pain intensity (SMD = −0.2711, 95% CI: −0.5109 to −0.0313, p = 0.267), functional impairment (SMD = −0.2508, 95% CI: −0.5574 to 0.0559, p = 0.1089), and mental health (SMD = −0.0328, 95% CI: −0.1972 to 0.1316, p = 0.6956). These findings indicate that AI-assisted physiotherapy had no demonstrable additional effect compared to usual physiotherapy for patients with LBP. Sensitivity analyses were conducted to address inter-study heterogeneity, confirming the robustness of these results. Conclusions: While AI-assisted physiotherapy shows potential in managing LBP by providing personalized treatment and feedback, the current evidence does not demonstrate significant advantages over usual physiotherapy. Further large-scale, long-term, and methodologically rigorous randomized controlled trials are necessary to validate these findings, assess their clinical relevance, and explore broader public health applications.
Date Issued
2025-02-01
Date Acceptance
2025-01-31
Citation
Applied Sciences, 2025, 15 (3)
ISSN
2076-3417
Publisher
MDPI AG
Journal / Book Title
Applied Sciences
Volume
15
Issue
3
Copyright Statement
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/).
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Subjects
AI-assisted physiotherapy
Chemistry
Chemistry, Multidisciplinary
DIAGNOSIS
Engineering
Engineering, Multidisciplinary
functional impairment
low back pain
Materials Science
Materials Science, Multidisciplinary
meta-analysis
pain management
Physical Sciences
Physics
Physics, Applied
Science & Technology
Technology
Publication Status
Published
Article Number
1532
Date Publish Online
2025-02-03