Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery.

Sushruta Koppula, Spandana Rajendra Kopalli, Sridevi Chigurupati, Amin Gasmi, Monica Gulati, Mayank Kundlas, Madhu Shukla, B Jayaprakash, Ankur Srivastava, Jayant Jagtap, Eiman Ibrahim, Prasanna Shama Khandige, Dario Salguero Garcia

Journal: Neuroscience 2025;572():214-231

PMID: 40068721

Full Text on Elsevier Science View on PubMed

Abstract

Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation. AI-driven imaging techniques, such as deep learning applied to CT and MRI scans, improve early diagnosis and identify ischemic penumbra, enabling timely, personalized interventions. AI-assisted decision support systems optimize acute stroke treatment, including thrombolysis and endovascular therapy. In motor rehabilitation, AI-powered robotics and exoskeletons provide precise, adaptive assistance, while AI-augmented Virtual and Augmented Reality environments offer immersive, tailored recovery experiences. Brain-Computer Interfaces utilize AI for neurorehabilitation through neural signal processing, supporting motor recovery. Machine learning models predict functional recovery outcomes and dynamically adjust therapy intensities. Wearable technologies equipped with AI enable continuous monitoring and real-time feedback, facilitating home-based rehabilitation. AI-driven tele-rehabilitation platforms overcome geographic barriers by enabling remote assessment and intervention. The review also addresses the ethical, legal, and regulatory challenges associated with AI implementation, including data privacy and technical integration. Future research directions emphasize the transformative potential of AI in stroke rehabilitation, with case studies and clinical trials illustrating the practical benefits and efficacy of AI technologies in improving patient recovery.

Address: Department of Bioscience and Biotechnology, Sejong University, Gwangjin-gu, Seoul 05006, Republic of Korea. Electronic address: [email protected].; Marwadi University Research Center, Department of Computer Engineering, Faculty of Engineering & Technology, Marwadi University, Rajkot 360003, Gujarat, India.; Department of Computer Science & IT, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India.; Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India.; Department of CSE, Chandigarh Engineering College, Chandigarh Group of Colleges-Jhanjeri, Mohali 140307, Punjab, India.; Department of Computing Science and Artificial Intelligence, NIMS Institute of Engineering and Technology, NIMS University Rajasthan, Jaipur, India.; School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab 1444411, India; ARCCIM, Faculty of Health, University of Technology Sydney, Ultimo, NSW 20227, Australia.; Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, Qassim University, Buraydah 51452, Saudi Arabia.; Department of Pharmacy Practice, College of Pharmacy, Qassim University, Buraydah 51452, Saudi Arabia.; NITTE (Deemed to be University) NGSM Institute of Pharmaceutical Sciences, Mangaluru, Karnartaka, India.; Department of Developmental and Educational Psychology, University of Almeria, Almeria, Spain.; College of Biomedical and Health Sciences, Konkuk University, Chungju-Si, Chungcheongbuk Do 27478, Republic of Korea.; International Institute of Nutrition and Micronutrition Sciences, Saint- Etienne, France; Société Francophone de Nutrithérapie et de Nutrigénétique Appliquée, Villeurbanne, France.

DOI: 10.1016/j.neuroscience.2025.03.017

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MeSH Terms: Humans, Stroke Rehabilitation, Artificial Intelligence, Recovery of Function, Stroke, Brain-Computer Interfaces