Mapping knowledge landscapes and emerging trends in artificial intelligence in glioblastoma: A bibliometric analysis.

Authors

Pei Wang, Wen Liu, Cong Zhou

Published in

Digital health. Volume 12. Pages 20552076261428356. Epub Feb 26, 2026.

Abstract

Glioblastoma, a highly aggressive and complex brain tumor, poses significant challenges in diagnosis and treatment. This bibliometric analysis aims to explore the trends and key players in artificial intelligence (AI)-driven glioblastoma research over the past two decades using the Web of Science core database.
This bibliometric analysis utilized the Web of Science Core Collection to identify English-language articles (n = 1487) published between 2004 and 2024, focusing on AI applications in glioblastoma. Data extraction and preliminary statistics were performed using Microsoft Excel. Subsequently, CiteSpace and VOSviewer were employed for in-depth analysis and visualization of research trends, collaboration networks, keyword cooccurrence, and emerging hotspots.
The results reveal a remarkable upward trend in research output, particularly after 2015, reflecting the growing interest in leveraging AI to address the complexities of glioblastoma. The United States and China were identified as the dominant contributors, with the University of Pennsylvania and Harvard Medical School emerging as key institutions. The analysis also identifies prominent authors such as Bakas Spyridon and highlights the pivotal role of journals like Neuro-Oncology and Cancers in disseminating cutting-edge research. Keyword analysis pinpointed "transfer learning" and "radiogenomics" as emerging frontiers, alongside established foci on machine/deep learning for imaging analysis and imaging biomarker development.
This comprehensive bibliometric study first delineates the evolving intellectual structure of AI in glioblastoma. It identifies the pivotal shift from methodological development to clinical translation and highlights radiogenomics as a key convergent frontier, providing a foundational map for future research prioritization.

PMID:
41767867
Bibliographic data and abstract were imported from PubMed on 26 Oct 2026.

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