AI-Driven Predictive Models for Personalized Rehabilitation and Assistive Systems

Abstract

The role of artificial intelligence (AI) during rehabilitation and assistive systems is to facilitate AI modeling, real-time environment monitoring, and personalized therapy solutions. Machine learning, deep learning, and reinforcement learning are used for AI-driven predictive models such as prediction of treatment plan optimization, timeline of recovery, and better patient outcomes. They are adaptive and intelligent rehabilitation using data from wearable sensors as well, medical imaging, and assistive robotics in an integrated fashion. Additionally, federated learning and the Internet of Medical Things (IoMT) also aid in providing privacy and security of data in a scalable and trustworthy rehabilitation solution. While decisions faced in the acquisition of data, security, or ethical issues can hinder the realization of a future with highly personalized, efficient, and accessible rehabilitation and assistive systems, AI is nevertheless advancing towards this type of system.