ArticleFrontiers in neurology2025
Artificial intelligence in glioma research: a bibliometric analysis of global trends, hotspots, and future directions.
Article in Frontiers in neurology, 2025. 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.
- Bibliometric Analysis of Artificial Intelligence in Pediatric Radiology and Medical Imaging: A Focus on Deep Learning Applications.Bioengineering (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Gliomas are the most common malignant primary brain tumors in adults and remain one of the greatest therapeutic challenges due to their infiltrative growth, molecular heterogeneity, and poor prognosis. With the rapid development of artificial intelligence (AI), increasing efforts have been made to apply AI tools across different stages of glioma research and clinical care. Objective: This study aims to provide a comprehensive bibliometric analysis of global research activity at the intersection of AI and gliomas, identifying leading contributors, emerging hotspots, and temporal trends. Methods: We systematically identified relevant publications from the past decade (January 2016 to June 2025) through searches of Web of Science, PubMed, and Scopus. The retrieved records underwent a rigorous de-duplication process and manual validation to ensure data integrity. Key bibliometric indicators were then extracted and analyzed using CiteSpace, Bibliometrix, and VOSviewer to evaluate publication growth trajectories, contributions by countries and institutions, journal co-citation networks, author influence, keyword evolution, and emerging research frontiers. Results: A total of 16,656 unique publications were identified, exceeding earlier bibliometric estimates. The cumulative number of publications exhibited exponential growth ( Conclusion: AI has become increasingly integrated into glioma research, complementing traditional diagnostic and therapeutic strategies. By highlighting global research patterns and emerging topics, this study provides valuable insights into the evolving landscape of AI applications in gliomas and suggests future directions for clinical translation.
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What OpenQuestion 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 OpenQuestion graph.