Artificial intelligence in head and neck cancer: a bibliometric analysis of research landscape, emerging trends, and challenges

• Published in: Frontiers in oncology Vol. 15; p. 1604136
• Main Authors: Liu, Shufang, Zhang, Jingdan, Tan, Ziye, Zhou, Bo
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 01.09.2025
• Subjects: artificial intelligence; bibliometric; deep learning; head and neck cancer; tumor; bibliometric; deep learning; head and neck cancer; tumor; artificial intelligence
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2025.1604136

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. This study aims to delineate the knowledge structure, hotspots, and trends in AI applications for head and neck cancers since 1995 through bibliometric analysis. 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. 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". 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.