Clinical Report: AI in Rehabilitation—Efficacy, Safety, and Equity Overview

Overview

Artificial intelligence (AI) and technology-assisted modalities show promise in extending rehabilitation reach and personalizing care, particularly for post-stroke upper limb activity improvement. However, clinical benefits beyond activity gains are inconsistent, and challenges remain in validation, safety, equity, and real-world deployment.

Background

Rehabilitation demand is rising globally, with over 2.4 billion people potentially benefiting from services. AI-enabled tools, including brain-computer interfaces, computer vision, and predictive analytics, are increasingly applied across rehabilitation settings to address capacity gaps. Despite rapid growth, evidence is fragmented, and concerns about generalizability, safety, usability, and equity persist. This umbrella review synthesizes systematic reviews to provide a consolidated clinical perspective on AI's role in rehabilitation.

Data Highlights

The umbrella review highlights that technology-assisted training, especially robotics with or without virtual reality, consistently improves post-stroke upper limb activity. Adverse events during telerehabilitation are rare and mostly mild (~0.3% of 84,534 sessions). However, AI-enabled systems like brain-computer interfaces show performance drops from development to deployment, limiting immediate clinical impact. Reporting on usability, adherence, equity, and cost remains insufficient, particularly in home and hybrid care models.

Key Findings

• Technology-assisted training improves post-stroke upper limb activity, but effects on impairment and independence are inconsistent when dose and blinding are controlled.
• Claims of AI non-inferiority lack rigorous prespecified margins and confidence-interval testing, often showing no clear advantage.
• Brain-computer interface classifiers and computer-vision movement evaluation suffer performance degradation in clinical deployment.
• Imaging-based AI decision support is closer to clinical use but requires local calibration and impact evaluation.
• Reported adverse events are generally mild and rare, but safety and usability data are underreported, especially for home-based rehabilitation.
• AI model reporting often falls short of standards; evidence is skewed toward high-income settings with limited subgroup performance data.

Clinical Implications

Clinicians should adopt AI-enabled rehabilitation adjunctively, ensuring interventions demonstrate clinically meaningful benefits under blinded, dose-matched conditions. External multi-site validation and continuous monitoring for performance loss and equity are essential before widespread implementation. Standardized safety and usability assessments, particularly for home and hybrid models, are critical to safeguard patients and optimize adherence.

Conclusion

AI has the potential to expand rehabilitation access and independence when held to rigorous clinical standards emphasizing validated benefit, safety, equity, and interoperability. Clear adoption criteria and ongoing post-market surveillance will be key to integrating AI effectively without compromising care quality.

References

1. Umbrella Review on AI in Rehabilitation, 2025 -- The Role of Artificial Intelligence in Rehabilitation: An Overview of Clinical Efficacy, Practical Application, Safety, and Equity