Neda M. Yousef

College of Health Sciences, Gulf Medical University, UAE

Abstract Title: Developing an artificial intelligence application for long-term rehabilitation in Neuro-Musculoskeletal Conditions: C.O.R.E (Comprehensive Ongoing Rehabilitation & Exercise)- A Prototype of a hybrid model

Biography: Neda M. Yousef is master’s student in physiotherapy based in the UAE, she is driven by a strong commitment to create and innovate solutions that bring meaningful impact to healthcare in the region. Her clinical practice blends evidence-based approaches with empathy, ensuring every intervention is tailored to patients’ individual needs. She has hands-on experience with advanced rehabilitation tools, including virtual reality systems, AI-driven assessment platforms, and robotic-assisted therapy devices, which she uses to enhance recovery and patient engagement. Passionate about bridging innovation and clinical care, she aspires to advance the UAE’s healthcare landscape by integrating emerging technologies into physiotherapy practice and research.

Research Interest: Neuro-musculoskeletal conditions affect over millions of people worldwide, with many facing a decline in function after discharge due to poor adherence to maintenance or home rehabilitation programs. There has been a significant growth in mobile health applications to address this problem; however, they rarely combine real-time adaptability, clinician oversight, and individualized exercise program prescriptions based on the subjective and objective needs of patients. This project introduces a new design for the development of C.O.R.E. (Comprehensive Ongoing Rehabilitation & Exercise). This is a hybrid model design that leverages AI-driven assessment and remote physiotherapist exercise prescription for the post-discharge population to enhance long-term rehabilitation and continuous care, and sustain recovery on the long term. This is a descriptive study design outlining the initial development of the C.O.R.E app, guided by a targeted review of peer-reviewed studies, between 2015 to 2025. The analysis identified persistent gaps in existing applications and design for post-discharge care. Current mHealth applications, while effective in improving access and motivation, often lack dynamic real-time adaptability and comprehensive evaluation based on both subjective and objective progress. In addition, they still fall short in integrating evidence-based clinical reasoning with AI-driven continuous adaptability and gamification. C.O.R.E presents a hybrid AI-driven solution designed to support post-discharge care by integrating AI and machine learning to monitor and assess patients, while the therapist prescribes exercises remotely. By combining the strengths of artificial intelligence, computer vision, and clinical reasoning, C.O.R.E. offers a novel approach that balances automation with human expertise.