Haoyang Liu, Qiurong Xie
The integration of artificial intelligence (AI) into rehabilitation science is revolutionizing traditional therapeutic models, offering innovative solutions that enhance the precision, efficiency, and accessibility of rehabilitation services. This review explores the diverse applications of AI in rehabilitation, focusing on key technologies such as machine learning, deep learning, computer vision, natural language processing, and robotics. A key innovation is the proposed AI-empowered rehabilitation model, which transforms fragmented processes into an interactive, adaptive system with real-time assessment during interventions. AI-driven advancements in impairment assessment, intervention planning and delivery, post-discharge care, and patient education are driving a shift from experience-driven to data-model-driven rehabilitation systems. Notable AI-driven applications include AI-powered exoskeletons for motor rehabilitation (e.g., in stroke recovery), NLP-driven cognitive therapy, and tele-rehabilitation platforms that enable remote monitoring and adaptive interventions. Despite these advancements, challenges remain, including data limitations, ethical concerns, regulatory requirements, and clinical integration barriers. Addressing these challenges requires interdisciplinary collaboration to ensure AI's responsible and effective deployment in rehabilitation. This review highlights the transformative potential of AI in rehabilitation and emphasizes the need for continued research and validation to optimize patient outcomes and accessibility.