Artificial intelligence in dentistry: A cross-domain bibliometric analysis of technologies, functional applications, and dental specialties

Abstract

Background/purpose Artificial intelligence is increasingly applied in dentistry across diagnosis, prediction, treatment planning, education, and patient communication. This study aimed to provide a structured cross-domain overview of dental AI research by integrating AI technologies, functional applications, and dental specialties within one bibliometric framework. Materials and methods AI-related dental publications from 2016 to 2026 were retrieved from the Web of Science Core Collection. Original and review articles were screened and classified by AI technology, functional application, and dental specialty. Bibliometric mapping, collaboration analysis, keyword co-occurrence, and heatmap-based cross-domain visualization were performed. Results A total of 920 publications were included. Publication output increased markedly after 2022, reaching the highest volume in 2025. Deep learning was the dominant technology, especially in imaging-centered functions such as detection, segmentation, classification, and diagnosis. Radiology, orthodontics, periodontics/implantology, and endodontics were the leading specialty domains, reflecting the central role of dental imaging in AI adoption. Machine learning was more frequently associated with prediction-oriented and public health applications, whereas natural language processing and large language models were increasingly linked to education, patient communication, knowledge retrieval, and decision support. Conclusion This study provides an integrated perspective on how dental AI is developing across clinical domains. The findings highlight established imaging-based pathways and emerging opportunities for multimodal, externally validated, and clinically meaningful AI systems, thereby informing future research planning and clinical translation in dentistry.