DefinePK

Abstract

Artificial Intelligence (AI) powered feedback has been recently integrated with motor learning and physiotherapy to enhance rehabilitation outcomes. Despite the emergence of AI applications in these disciplines, there are still several hurdles to overcome such as integration with real-world applications, accessibility, feedback precision, and data security (up to October 2023). This study seeks to tackle these issues through the investigation of creative methods of embedding AI feedback systems within the clinical physiotherapy workflow. This is vital, as it makes sure that AI systems will cater to the diverse needs of patients, thus enhancing the efficiency of therapy. Second, this research will explore ethical facets of such AI feedback by creating privacy-preserving models; transparent data-sharing protocols and collaborative practices that engender patient trust. The application of AI-supported feedback will further inform building affordable, scalable, and usable systems, especially for underrepresented patient groups. A step towards clinical validation of AI-based rehabilitation as well as personalized system designs, this study aims to push AI rehabilitation forward to serve as a scalable solution for enhancing motor learning and improving physiotherapy outcomes.