This bibliometric study introduces a novel approach to assessing the application of artificial intelligence (AI) in dentistry. It analyzes trends in AI utilization across dental disciplines, treatment stages, data modalities, subsets, models, and tasks and proposes a comprehensive classification framework for AI applications in dentistry. A systematic search in the Web of Science Core Collection on December 1, 2024, using AI- and dentistry-related keywords identified original and review articles employing true AI. Data on publication details, study types, dental disciplines, treatment stages, AI subsets, models, data modalities, and tasks were extracted and analyzed using VOSviewer (Leiden University, Leiden, Netherlands) and Microsoft Excel (Microsoft Corp., Redmond, WA). Trend analysis and forecasting methods were applied to identify future research directions. Of 2,810 records, 1,368 studies met the inclusion criteria, revealing a continuous rise in AI-related dental research. While most studies focused on diagnostic applications and the orthodontics discipline, the highest recent growth was seen in treatment planning and research and education applications. Hybrid AI models and natural language processing (NLP) experienced significant increases in adoption. The most common AI tasks were classification, detection, and segmentation, although notable growth occurred in generation, data integration, and decision support. The classification framework for AI in dentistry is presented. Text-based data have shown the greatest growth among data modalities, alongside an increased use of sensor and signal data. Future research should prioritize developing NLP and hybrid AI models, conducting original studies in research and education and treatment planning, and undertaking systematic reviews focused on the diagnosis stage of prosthodontics and endodontics.