## 5 Strengths and Limitations  
This systematic review and meta-analysis exhibit several notable strengths. First, the study employed a comprehensive and systematic search strategy across four major scientific databases, ensuring the inclusion of diverse comparative studies on AI-assisted physiotherapy for non-specific low back pain (NSLBP). This approach resulted in the inclusion of eight studies for systematic review and six for meta-analysis, providing a robust dataset for analysis. Furthermore, this review uniquely focuses exclusively on the effects of AI-assisted physiotherapy for NSLBP, addressing a critical gap in the literature. Unlike prior reviews that broadly grouped various digital interventions, such as AI-based, video-based, and web-based treatments, this review isolated AI-assisted physiotherapy to provide a targeted and nuanced evaluation of its potential benefits. By focusing on AI-specific approaches, this review highlights the distinct capabilities of AI models, such as real-time adaptive feedback, personalized treatment plans, and remote monitoring, which are less feasible with traditional digital methods. These contributions not only enhance our understanding of the unique role of AI in physiotherapy but also offer a valuable foundation for future research and clinical applications aimed at integrating AI-driven solutions into standard care practices. The study also adopted rigorous methodologies to ensure accuracy and reliability. A double-checking strategy was implemented during full-text screening and data extraction, with two independent reviews conducted at each stage. This approach minimized errors and enhanced data completeness. Additionally, the use of Google search verification ensured that only studies explicitly involving AI-assisted physiotherapy were included, excluding other forms of interventions and reinforcing the specificity and accuracy of the review. Despite these strengths, the study had significant limitations. Sample size constraints were a significant limitation of this study, as the relatively small number of available studies on AI-assisted physiotherapy for NSLBP restricted the robustness of the analysis. Variability in participant numbers across studies further exacerbated this issue, leading to imprecise results reflected in wider 95% confidence intervals. This imprecision limits the strength of the conclusions that can be drawn and underscores the need for careful interpretation of the findings. Additionally, differences in intervention protocols, including variations in AI technologies used, treatment durations, and assessment methods, may have contributed to heterogeneity, further challenging the generalizability of the results. Future research should prioritize larger, well-designed randomized controlled trials with consistent intervention protocols to enhance precision and provide more definitive evidence. Furthermore, studies exploring the underlying mechanisms of AI integration in physiotherapy could inform tailored interventions, optimize outcomes, and address the gaps identified in this analysis. Heterogeneity among studies was another limitation, arising from variations in study designs, intervention frequencies, treatment software, and the use of sensors. These inconsistencies prevented systematic analysis of these influencing factors and increased uncertainty in the interpretation of results. Additionally, most included studies relied on self-reported outcome data, which is subject to response bias, potentially affecting the reliability of findings. Moreover, none of the included studies monitored participants' use of additional healthcare services during the study period. This lack of monitoring made it unclear whether other treatments were received, introducing the possibility of confounding effects that could skew results. The inability to account for these variables limits the study's ability to fully isolate the effects of AI-assisted physiotherapy.