{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T09:49:24Z","timestamp":1791625764985,"version":"4.3.4"},"reference-count":42,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Stroke remains a leading cause of long\u2010term disability worldwide, and rehabilitation is essential for recovery. Although artificial intelligence (AI)\u2010related technologies have received growing attention in stroke rehabilitation, the knowledge structure and thematic evolution of this interdisciplinary field remain unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To conduct a bibliometric analysis of AI\u2010related research in stroke rehabilitation from 2005 to 2024 and map publication trends, major contributors, thematic clusters, and emerging topics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Relevant publications were retrieved from the Web of Science Core Collection (WoSCC), including SCI\u2010Expanded and SSCI, on November 30, 2024. Only English\u2010language 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\u2010occurrence, clustering, and burst detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Publication 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 Calabr\u00f2 was among the most productive and highly cited authors. Core publication venues included the\n                      <jats:italic>Journal of NeuroEngineering and Rehabilitation<\/jats:italic>\n                      and\n                      <jats:italic>IEEE Transactions on Neural Systems and Rehabilitation Engineering<\/jats:italic>\n                      . The literature mainly focused on virtual reality, upper\u2010limb 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\u2010driven rehabilitation approaches.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI\u2010related research in stroke rehabilitation has expanded substantially, with increasing emphasis on adaptive, data\u2010driven, and technology\u2010assisted 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.71451","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T16:54:36Z","timestamp":1777308876000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence in Stroke Rehabilitation: A 20\u2010Year Bibliometric Analysis of Digital Health Trends and Technologies"],"prefix":"10.1002","volume":"16","author":[{"given":"Yuhua","family":"Li","sequence":"first","affiliation":[{"name":"Department of Neurology Qingyang People's Hospital  Qingyang Gansu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Neurology Qingyang 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