ArticleDigital health
Mapping knowledge landscapes and emerging trends in artificial intelligence in glioblastoma: A bibliometric analysis.
Article in Digital health. 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.
- Global Research Trends in Pediatric Lower Urinary Tract Dysfunction: A Bibliometric Analysis (1945-2026).Healthcare (Basel, Switzerland) · 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
Background: 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. Methods: This bibliometric analysis utilized the Web of Science Core Collection to identify English-language articles ( Results: 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 Conclusions: 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.
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.