Accessibility settings

This paper is in the following e-collection/theme issue:

Scientometrics, Infometrics, and Altmetrics (492) Knowledge Translation and Implementation Science (813) Stroke (331) Digital Health Meta-Research and Bibliographic Studies (122) Generative Language Models Including ChatGPT (1633) AI Language Models in Health Care (828)

Published on in Vol 28 (2026)

This is a member publication of Imperial College London (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70754, first published .
Human brain split: organic left hemisphere, digital right hemisphere with circuits

Quantifying Innovation in Stroke: Large Language Model Bibliometric Analysis

Quantifying Innovation in Stroke: Large Language Model Bibliometric Analysis

Authors of this article:

Adam Marcus1, 2 Author Orcid Image ;   Georgina Lockwood-Taylor3 Author Orcid Image ;   Daniel Rueckert1, 4 Author Orcid Image ;   Paul Bentley2 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

Journals

  1. Mo C, Manchaiah V, Wasmann J, Wang S. From policy debate to empirical evidence: a proof-of-concept bibliometric and LLM-assisted abstract-level content analysis of OTC hearing aid research before and after FDA regulation. International Journal of Audiology 2026:1 View
  2. Ma W, Nie P, Yin R, Li L, Wang L. Beyond keywords: Semantic classification with large language models for mapping synthetic biology literature. Biosafety and Health 2026 View
  3. Taheripour E, Tavanarezaei M, Golabzaei M. A bibliometric analytics review of three decades of artificial intelligence research in stroke prediction. Healthcare Analytics 2026;10:100498 View