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

Scientometrics, Infometrics, and Altmetrics (126) Digital Health Meta-Research and Bibliographic Studies (64) Stroke (253) Generative Language Models Including ChatGPT (747) AI Language Models in Health Care (216) Knowledge Translation and Implementation Science (532)

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 .
Quantifying Innovation in Stroke: Large Language Model Bibliometric Analysis

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 Tweetations Metrics

Adam Marcus   1, 2 , BSc, MSc, MBBS, PhD ;   Georgina Lockwood-Taylor   3 , MSc ;   Daniel Rueckert   1, 4 , PhD ;   Paul Bentley   2 , PhD, MRCP

1 Department of Computing, Imperial College London, London, United Kingdom

2 Department of Medicine, Imperial College London, London, United Kingdom

3 Department of Psychology and Neuroscience, King's College London, London, United Kingdom

4 Klinikum rechts der Isar, Technical University of Munich, Munich, Germany

Corresponding Author: