Comprehensive Summary
This research looks at the overall evolution and current application of artificial intelligence in stroke rehabilitation. It was done through the analysis of technological developments from the basic computer aided method to a sophisticated AI-integrated system. The authors created a historical progression and then noted the current research status. Additionally, they analyzed future trends through the incorporation of data from multiple meta-analyses and randomized controlled trials from across the field. Findings showed that AI development in stroke rehabilitation has gone through three distinct phases: germination, technology integration and initial application. Clinical corroborate studies have shown notable therapeutic benefits, with the robot assisted rehabilitation producing quantifiable improvements in upper limb function, with SMD= 0.29, 95% CI 0.14-0.44 across 52 RCTs involving 2,774 patients. VR-based AI systems have achieved superior outcomes compared to traditional therapy, including upper limb Fugl-Meyer Assessment scores of 47.46 ± 0.48 versus 41.82 ± 0.79 in control groups. The authors deduce that AI is truly transforming stroke rehabilitation from standardized and reactive care toward personalized and proactive care. However, there are challenges that persist, such as data standardization, ethical frameworks, and healthcare system integration.
Outcomes and Implications
This research addresses the critical global health challenge, because stroke remains one of the leading causes for different types of disabilities internationally. The traditional rehabilitation approaches are often constrained by the limited efficiency and lack of individualization for patient-specific recovery needs. The clinical relationship is significant and has immediate application. Multiple technologies have been used and showed qualitative patient outcomes in a clinical setting. Robot-assisted training systems are being implemented in rehabilitation centers, with wearable sensors and brain-computer interfaces allowing for real-time monitoring and personalized treatments. The authors speculate that widespread clinical adoption is progressing rather quickly with remote and home-based AI rehabilitation systems showing the immediate future of stroke rehabilitation. In addition to being supported by cloud computing platforms that allow healthcare providers to monitor patient progress and refine treatment plans in real-time. This can transform access to specialized rehabilitation services for patients in underserved or geographically isolated areas.