Journal of NeuroEngineering and Rehabilitation· 2026Q1· Review
Technological frontiers in neuropsychological rehabilitation: a bibliometric-systematic literature review of AI applications for pediatric, adult, and geriatric populations
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- Q1SCImago
- 2026year
Short summary
A bibliometric review of 4336 publications (2020-2025) reveals AI applications in neuropsychological rehabilitation are heavily skewed towards geriatrics (3,087 papers) compared to adults (883) and pediatrics (366), with virtual reality and machine learning being central technologies.
AI-generated from the title and abstract; the full text is not read.
Key points
- Analysis of 4336 publications (2020-2025) on AI in neuropsychological rehabilitation.
- Geriatric research (3,087 papers) dominates over adult (883) and pediatric (366) research.
- Virtual reality is a key technology, applied differently across age groups (e.g., executive functions in pediatrics, cognitive assessment in geriatrics).
- Working-age adults with acquired brain injuries are an underserved population.
- High-impact areas include AI neurofeedback in pediatrics and VR/ML for mild cognitive impairment in geriatrics.
AI-generated from the title and abstract; the full text is not read.
Abstract
The integration of artificial intelligence (AI) into neuropsychological rehabilitation represents an expanding technological frontier, yet no study has systematically mapped this field across the lifespan. This bibliometric-systematic review analyzed 4336 Scopus-indexed publications (2020–2025) across pediatric ( n = 366), adult ( n = 883), and geriatric ( n = 3,087) populations using Bibliometrix. Results reveal an overwhelming disparity: research on older adults exceeds that on children and adults by eightfold. Geriatric research shows sustained annual growth (≈ 18.5 articles/year), while pediatric and adult corpora exhibit volatile, non-linear patterns (R²=0.0515 and 0.0087), indicating lack of research consolidation. Virtual reality emerged as the central cross-cutting technology, yet with marked specialization: in pediatrics, associated with executive functions training; in older adults, combined with machine learning for cognitive assessment (highest impact: 4.250). Thematic foci align with epidemiological priorities—neurodevelopmental disorders in children, acquired conditions in adults, neurodegenerative diseases in older adults—while revealing methodological stratification: geriatric research integrates neuroimaging with AI; pediatric research emphasizes behavioral interventions. High-impact niches identified include AI-integrated neurofeedback in pediatrics (impact 2.052) and VR with machine learning for mild cognitive impairment in gerontology (impact 4.250). Working-age adults with acquired brain injuries emerge as a persistently underserved population. This first comparative cartography of AI-enabled neuropsychological rehabilitation across the lifespan offers actionable insights for strategic resource allocation and identifies critical gaps requiring urgent attention.
The authors' abstract, as published at the source. Journal of NeuroEngineering and Rehabilitation, 2026 · DOI ↗
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Field: Rehabilitation
RehabilitationMedicine