Evidence map›Paper›PMID 41284199›Full record

ArticleDiscover oncology2025

Artificial intelligence in head and neck cancer: a bibliometric and visualization analysis (1995-2025).

Chuyi Cai, Jingfeng Zhang, Chunshu Pan, Zachary James Drew, Xiaohui Wang, Qi Dai, Guoping Chen, Kai Li

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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

8 authors.

Chuyi CaiDepartment of Radiology , Ningbo No.2 Hospital , Zhejiang, 315010, Ningbo, China.
Jingfeng ZhangDepartment of Radiology , Ningbo No.2 Hospital , Zhejiang, 315010, Ningbo, China.
Chunshu PanDepartment of Radiology , Ningbo No.2 Hospital , Zhejiang, 315010, Ningbo, China.
Zachary James DrewDepartment of Medical Imaging , Royal Brisbane and Women's Hospital , Herston, Australia, QLD, 4006.
Xiaohui WangDepartment of Radiology , Ningbo No.2 Hospital , Zhejiang, 315010, Ningbo, China.
Qi DaiDepartment of Radiology , Ningbo No.2 Hospital , Zhejiang, 315010, Ningbo, China.
Guoping ChenDepartment of Radiology , Ningbo No.2 Hospital , Zhejiang, 315010, Ningbo, China.
Kai LiChemo & Radiotherapy Dept.2 , Ningbo No.2 Hospital , Ningbo, China, Zhejiang, 315010. likai0507@163.com.

Funding

Medical Scientific Research Foundation of Zhejiang Province 2020KY841Ningbo Clinical Research Center for Medical Imaging 2021L003Project of National Key Clinical Specialty (Department of Medical Imaging 2024017NINGBO
6 · The paper itself

Abstract

objectivesHead and neck cancers (HNCs) pose significant challenges for clinical diagnosis and treatment due to their complex anatomical structures, atypical early symptoms, and the considerable morbidities associated with treatment. The rapid development of artificial intelligence (AI) technologies in medicine has introduced a new paradigm for the precise diagnosis and management of HNCs. MATERIALS AND

methodsThis study used bibliometric methods to systematically analyze the research landscape of AI applications in HNCs from 1995 to 2025. The aim was to identify research trends, collaboration networks, and emerging directions, thereby providing a reference for future investigations.

resultsA total of 230 AI-related publications on HNCs were retrieved from the Web of Science database. Tools such as CiteSpace and VOSviewer were used to analyze temporal publication trends, national and institutional contributions, core author groups, journal distribution, and keyword clustering. Key milestone studies and the evolution of research hotspots were identified through co-citation analysis and burst keyword detection.

conclusionAI research in HNCs has evolved into a multimodal and multi-task field, with deep learning playing a central role in image analysis. However, challenges persist regarding model interpretability and generalizability. CLINICAL RELEVANCE: In the future, AI applications in HNCs are expected to further enhance diagnostic and therapeutic strategies. Strengthening interdisciplinary collaboration is essential to translate AI algorithms into comprehensive, end-to-end clinical applications. Such integration will optimize the entire care pathway for head and neck cancer patients.

Indexed as

Artificial intelligenceBibliometricDeep learningHead and neck cancerMachine learning

Identifiers

PMID41284199
PMCPMC12644365

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.