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

Area of Science:

• Medical Informatics
• Oncology
• Artificial Intelligence

Background:

• Head and neck cancers (HNCs) present diagnostic and treatment challenges due to complex anatomy and treatment morbidities.
• Artificial intelligence (AI) offers a new approach for precise HNC diagnosis and management.

Purpose of the Study:

• To systematically analyze the research landscape of AI applications in HNCs using bibliometric methods.
• To identify research trends, collaboration networks, and emerging directions in AI for HNCs.

Main Methods:

• Bibliometric analysis of 230 AI-related publications on HNCs (1995-2025) from Web of Science.
• Utilized CiteSpace and VOSviewer for analyzing publication trends, contributions, authors, journals, and keywords.
• Identified key studies and research hotspots through co-citation and burst keyword analysis.

Main Results:

• AI research in HNCs is multimodal and multi-task, with deep learning dominating image analysis.
• Analysis revealed publication trends, national/institutional contributions, core authors, and journal distribution.
• Key milestone studies and evolving research hotspots were identified.

Conclusions:

• AI research in HNCs is advancing, with deep learning crucial for image analysis.
• Challenges remain in model interpretability and generalizability.
• Future AI applications require interdisciplinary collaboration for enhanced diagnosis, therapy, and integrated clinical care pathways.