ReviewBrain and behavior2026
Artificial Intelligence in Stroke Rehabilitation: A 20-Year Bibliometric Analysis of Digital Health Trends and Technologies.
Review in Brain and behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence in rheumatology: a cross-sectional Scopus-based analysis.Rheumatology international · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStroke remains a leading cause of long-term disability worldwide, and rehabilitation is essential for recovery. Although artificial intelligence (AI)-related technologies have received growing attention in stroke rehabilitation, the knowledge structure and thematic evolution of this interdisciplinary field remain unclear.
objectiveTo conduct a bibliometric analysis of AI-related research in stroke rehabilitation from 2005 to 2024 and map publication trends, major contributors, thematic clusters, and emerging topics.
methodsRelevant publications were retrieved from the Web of Science Core Collection (WoSCC), including SCI-Expanded and SSCI, on November 30, 2024. Only English-language articles and review articles published between January 1, 2005, and November 30, 2024 were included. A total of 3436 records were analyzed using CiteSpace 6.4.R1 Basic, GraphPad Prism 10.1.2, and biblioshiny in R. Analyses covered publication trends, collaboration networks, journal distribution, keyword co-occurrence, clustering, and burst detection.
resultsPublication output increased markedly over time, with the United States contributing the largest number of publications. The Swiss Federal Institutes of Technology Domain was among the leading institutions, and Rocco Salvatore Calabrò was among the most productive and highly cited authors. Core publication venues included the Journal of NeuroEngineering and Rehabilitation and IEEE Transactions on Neural Systems and Rehabilitation Engineering. The literature mainly focused on virtual reality, upper-limb rehabilitation, rehabilitation robotics, machine learning, cognitive rehabilitation, and transcranial direct current stimulation. Recent burst terms, including machine learning, artificial intelligence, and deep learning, indicated growing attention to data-driven rehabilitation approaches.
conclusionsAI-related research in stroke rehabilitation has expanded substantially, with increasing emphasis on adaptive, data-driven, and technology-assisted approaches. This study provides a descriptive overview of the field's major trajectories, emerging gaps, and interdisciplinary directions, and may help inform future research and translational exploration.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.