Artificial Intelligence and the Future of Stroke Rehabilitation in Nigeria: Opportunities for Personalized Physical Therapy, Clinical Decision Support, Digital Patient Monitoring, Health Data Utilization, and Improved Continuity of Rehabilitation Car

Background. Stroke is a leading cause of long-term disability, and rehabilitation led by physical therapy is central to recovery. In Nigeria, rehabilitation is limited by few specialized services, uneven geographical distribution of health workers and facilities, and weak follow-up and data systems. These problems affect how progress is assessed, monitored and continued after hospital discharge, and they raise the question of whether artificial intelligence (AI) could help. Objective. To review how AI might support physical therapy and stroke rehabilitation in Nigeria, and to examine the opportunities, limitations, ethical considerations and implementation challenges. Review approach. This is a narrative literature review of existing academic and institutional evidence. It is not a systematic review and involved no original data collection or AI development. International, other African and Nigerian evidence is distinguished throughout. Main findings. International reviews describe AI applications in movement and gait assessment, outcome prediction, exercise monitoring and remote rehabilitation, with promising but uneven evidence. A mapping review found that external validation and explainability were rarely reported (Morone et al., 2025). No Nigerian evaluation of AI-assisted stroke rehabilitation was identified among the literature reviewed. Nigerian evidence relates mainly to rehabilitation access, feasibility of low-technology telerehabilitation and the state of stroke services and data. Barriers include unreliable power and connectivity, cost, workforce training, data quality, privacy and the under-representation of African populations in AI development. Conclusion. AI has potential to support more personalized assessment, monitoring, decision support, use of rehabilitation data and continuity of care. It should augment, not replace, physical therapists. Realistic progress in Nigeria depends on local evidence, infrastructure, training, data quality, privacy protection, affordability, equity and human clinical oversight.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-26
DOI
https://doi.org/10.5281/zenodo.23661082
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
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article

Artificial Intelligence and the Future of Stroke Rehabilitation in Nigeria: Opportunities for Personalized Physical Therapy, Clinical Decision Support, Digital Patient Monitoring, Health Data Utilization, and Improved Continuity of Rehabilitation Car

Osungbure Oluwakemi Esther
Zenodo (CERN European Organization for Nuclear Research)
Stroke Rehabilitation and Recovery
article

Artificial Intelligence and the Future of Stroke Rehabilitation in Nigeria: Opportunities for Personalized Physical Therapy, Clinical Decision Support, Digital Patient Monitoring, Health Data Utilization, and Improved Continuity of Rehabilitation Car

Osungbure Oluwakemi Esther
article en

Abstract

Background. Stroke is a leading cause of long-term disability, and rehabilitation led by physical therapy is central to recovery. In Nigeria, rehabilitation is limited by few specialized services, uneven geographical distribution of health workers and facilities, and weak follow-up and data systems. These problems affect how progress is assessed, monitored and continued after hospital discharge, and they raise the question of whether artificial intelligence (AI) could help. Objective. To review how AI might support physical therapy and stroke rehabilitation in Nigeria, and to examine the opportunities, limitations, ethical considerations and implementation challenges. Review approach. This is a narrative literature review of existing academic and institutional evidence. It is not a systematic review and involved no original data collection or AI development. International, other African and Nigerian evidence is distinguished throughout. Main findings. International reviews describe AI applications in movement and gait assessment, outcome prediction, exercise monitoring and remote rehabilitation, with promising but uneven evidence. A mapping review found that external validation and explainability were rarely reported (Morone et al., 2025). No Nigerian evaluation of AI-assisted stroke rehabilitation was identified among the literature reviewed. Nigerian evidence relates mainly to rehabilitation access, feasibility of low-technology telerehabilitation and the state of stroke services and data. Barriers include unreliable power and connectivity, cost, workforce training, data quality, privacy and the under-representation of African populations in AI development. Conclusion. AI has potential to support more personalized assessment, monitoring, decision support, use of rehabilitation data and continuity of care. It should augment, not replace, physical therapists. Realistic progress in Nigeria depends on local evidence, infrastructure, training, data quality, privacy protection, affordability, equity and human clinical oversight.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 16%
Stroke Rehabilitation and Recovery
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