Discover oncology · 2025
Artificial intelligence in head and neck cancer: a bibliometric and visualization analysis (1995-2025).
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Cai Chuyi, Zhang Jingfeng, Pan Chunshu, Drew Zachary James, Wang Xiaohui, Dai Qi, Chen Guoping, Li Kai
Аннотация
OBJECTIVES: Head 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 METHODS: This 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.
RESULTS: A 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.
CONCLUSION: AI 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.
MATERIALS AND METHODS: This 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.
RESULTS: A 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.
CONCLUSION: AI 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.