ArticleFrontiers in oncology2025
Artificial intelligence in head and neck cancer: a bibliometric analysis of research landscape, emerging trends, and challenges.
Article in Frontiers in oncology, 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.
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: Head and neck cancer is the seventh most common cancer worldwide. As an aggressive malignancy, it is characterized by high metastasis rates, complex anatomy, challenging treatments, high recurrence rates, and significant disability. Over the past decade, advancements in big data, AI algorithms, and hardware have enabled artificial intelligence to make substantial contributions to addressing medical challenges in oncology, including head and neck cancer. The era of AI-driven head and neck tumor management may soon arrive. Despite significant attention, there has been a lack of quantitative literature-based studies in this field. Objective: This study aims to delineate the knowledge structure, hotspots, and trends in AI applications for head and neck cancers since 1995 through bibliometric analysis. Method: We conducted a comprehensive literature search via the Web of Science, utilizing tools such as CiteSpace, ArcGIS, and VOSviewer for analysis, with a focus on key countries, institutions, authors, and emerging topics. Result: We analyzed 362 papers authored by 235 researchers from 189 institutions across 55 countries, with China leading in publication output. Radiotherapy and Oncology was the most influential journal. Bur, Andres M was the pioneering author, and the University of Texas System ranked as the top publishing institution. Currently, the most significant keywords include "target volumes," "prognosis," "algorithm," "survival," "lesions," and "automatic diagnosis." Additionally, we identified 12 keyword clusters in the field, with the latest five clusters labeled as "automatic diagnosis", "explainable artificial intelligence", "guidelines", "research trends", and "natural intelligence". Conclusion: This article provides a concise overview of the current landscape and emerging trends in AI applications for head and neck cancer research, offering insights and guiding future studies in this evolving field.
Indexed as
Identifiers
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.