## 4 Discussion  
This systematic review and meta-analysis evaluated the efficacy of AI-assisted physiotherapy for managing non-specific low back pain (NSLBP). The findings suggest that AI-assisted physiotherapy showed some advantages over usual physiotherapy in reducing  
Figure 5: (a,b) Mental Health Outcomes: Forest Plot, Funnel Plot, and Sensitivity Analysis Comparing AI-Assisted Physiotherapy to Usual Therapeutic Interventions [30; 31; 33; 34; 35]. post-intervention pain intensity and functional impairment and improving psychological well-being. However, these differences were not statistically significant. These results highlight the potential benefits of integrating AI technologies in physiotherapy while emphasizing the need for further research to establish robust and consistent evidence. AI-assisted physiotherapy has garnered increasing attention in recent years, expanding its application beyond NSLBP to conditions such as shoulder, knee, and ankle rehabilitation. However, systematic reviews specifically addressing AI-assisted physiotherapy for NSLBP remain scarce. This study fills a critical gap by providing a comprehensive meta-analysis focused on this intervention. Previous systematic reviews, such as [PERSON] et al. (2022) [26], examined the effectiveness of mHealth self-management apps for LBP. Their findings, consistent with this study, showed encouraging results in pain reduction. However, the scope and methodology of [PERSON] et al. differ significantly from this study. While their review included mobile apps with various functionalities beyond AI assistance, this study focused solely on AI-assisted interventions. Additionally, [PERSON] et al. included controls such as web-based email support and placebo applications, whereas this review strictly compared AI-assisted physiotherapy to usual physiotherapy. Differences in study populations, pain types (acute, subacute, or chronic), and intervention designs also contribute to the discrepancies between findings. Other reviews, such as [PERSON] et al. (2021) [25], examined digital therapeutic care broadly, including internet-based platforms, text-based chatbots, and interactive apps. Their findings indicated significant benefits for LBP, but methodological limitations, including the inclusion of non-randomized studies and non-clinical guideline-based controls, reduce their generalizability compared to this study. Similarly, [PERSON] et al. [36] evaluated telemedicine for chronic pain but included a wider scope, incorporating web-based and mHealth interventions. While they reported clinically significant effects, this study offers a more focused assessment of AI-specific physiotherapy. By integrating recently published studies and narrowing its scope to AI-assisted physiotherapy, this meta-analysis provides an updated perspective on its effectiveness compared to usual physiotherapy. This is the first meta-analysis to specifically assess AI-assisted physiotherapy for NSLBP, highlighting its unique contributions to existing literature. The meta-analysis revealed no significant therapeutic effects of AI-assisted physiotherapy on pain intensity, functional impairment, or mental health compared to usual physiotherapy. This contrasts with some prior reviews, potentially due to differences in study populations, intervention designs, and outcome measures. For example, the study by [PERSON] et al. [33] noted that high baseline functional capacity in participants may have limited improvements observed over the study period, a phenomenon known as the ceiling effect. Heterogeneity in the results was notable, with variations in study designs, sample sizes, and intervention protocols. Small sample sizes in several studies likely amplified individual differences, contributing to higher variability. Moreover, as AI physiotherapy is an emerging technology, inconsistent implementation and learning curves among researchers and clinicians may have further influenced outcomes.