AI Article Synopsis
- - The study examines the potential of AI-supported physical rehabilitation technology to improve availability, clinical effects, and implementation barriers, addressing the growing need for rehabilitation as physical disabilities increase with age.
- - A systematic review identified 9054 articles, ultimately including 28 projects that spanned various AI solutions, such as app-based systems and robotic devices, but found inconsistent clinical outcomes from the five randomized controlled trials (RCTs) analyzed.
- - While AI in rehabilitation shows promise, challenges such as technology literacy and user fatigue hinder implementation, necessitating more rigorous clinical evaluations and real-world assessments to enhance its effectiveness.
Article Abstract
Background: Physical disabilities become more common with advancing age. Rehabilitation restores function, maintaining independence for longer. However, the poor availability and accessibility of rehabilitation limits its clinical impact. Artificial Intelligence (AI) guided interventions have improved many domains of healthcare, but whether rehabilitation can benefit from AI remains unclear.
Methods: We conducted a systematic review of AI-supported physical rehabilitation technology tested in the clinical setting to understand: 1) availability of AI-supported physical rehabilitation technology; 2) its clinical effect; 3) and the barriers and facilitators to implementation. We searched in MEDLINE, EMBASE, CINAHL, Science Citation Index (Web of Science), CIRRIE (now NARIC), and OpenGrey.
Results: We identified 9054 articles and included 28 projects. AI solutions spanned five categories: App-based systems, robotic devices that replace function, robotic devices that restore function, gaming systems and wearables. We identified five randomised controlled trials (RCTs), which evaluated outcomes relating to physical function, activity, pain, and health-related quality of life. The clinical effects were inconsistent. Implementation barriers included technology literacy, reliability, and user fatigue. Enablers included greater access to rehabilitation programmes, remote monitoring of progress, reduction in manpower requirements and lower cost.
Conclusion: Application of AI in physical rehabilitation is a growing field, but clinical effects have yet to be studied rigorously. Developers must strive to conduct robust clinical evaluations in the real-world setting and appraise post implementation experiences.
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Source |
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| http://dx.doi.org/10.1016/j.artmed.2023.102693 | DOI Listing |
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