Evidence map›Paper›PMID 40721908›Full record

ArticleDiscover oncology2025

A bibliometric analysis reveals a dynamic growth in the use of artificial intelligence in oral cancer research over three decades.

Irna Sufiawati, Anisa Insyafiana, Rifat Rahman, Adi Idris

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Irna Sufiawati *Department of Oral Medicine, Faculty of Dentistry, Universitas Padjadjaran, Bandung, West Java, Indonesia.
Anisa Insyafiana *Department of Oral Medicine, Faculty of Dentistry, Universitas Padjadjaran, Bandung, West Java, Indonesia.
Rifat Rahman *Institute for Biomedicine and Glycomics, School of Pharmacy and Medical Sciences, Griffith University, Southport, QLD, Australia.
Adi IdrisCentre for Immunology and Infection Control, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia. a2.idris@qut.edu.au.

Funding

National Health and Medical Research Council 2027569
6 · The paper itself

Abstract

Oral cancer (OC) remains a significant malignant neoplasm in both the developed and developing world. Artificial intelligence (AI) has had a significant impact on scientific disciplines, including oncology by transforming data analysis and predictive capabilities. Recent advancements in AI have enabled researchers to integrate and synthesize multidimensional datasets, infer patterns, and predict outcomes, ultimately enhancing shared decision-making between patients and clinicians. This bibliometric analysis aims to provide a comprehensive overview of the application of AI in OC research over the last three decades. Our analysis of 351 articles retrieved from SCOPUS between 1998 and 2024 using VOSviewer highlights the dynamic growth of AI in OC research. The significant trends in publications and citations reflect the increasing interest and impact of this field. These findings provide valuable insights for policymakers, funding agencies, and researchers, guiding future efforts to integrate AI technologies into oral oncology practices.

Indexed as

Artificial intelligenceBibliometric analysisOral cancerResearch trendsVOSviewer

Identifiers

PMID40721908
PMCPMC12304344

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.