Shirani Mohammadjavad
Department of Restorative Dentistry, Kornberg School of Dentistry, Temple University, Philadelphia, USA.
Cureus. 2025 Apr 7;17(4):e81836. doi: 10.7759/cureus.81836. eCollection 2025 Apr.
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.
这项文献计量学研究引入了一种评估人工智能（AI）在牙科领域应用的新方法。它分析了牙科各学科、治疗阶段、数据模式、子集、模型和任务中人工智能利用的趋势，并提出了一个用于牙科人工智能应用的综合分类框架。2024年12月1日，在科学网核心合集中使用与人工智能和牙科相关的关键词进行系统检索，确定了采用真正人工智能的原创文章和综述文章。使用VOSviewer（荷兰莱顿大学）和Microsoft Excel（微软公司，华盛顿州雷德蒙德）提取并分析了关于出版细节、研究类型、牙科学科、治疗阶段、人工智能子集、模型、数据模式和任务的数据。应用趋势分析和预测方法来确定未来的研究方向。在2810条记录中，1368项研究符合纳入标准，揭示了与人工智能相关的牙科研究持续增长。虽然大多数研究集中在诊断应用和正畸学科，但最近在治疗计划以及研究和教育应用方面增长最为显著。混合人工智能模型和自然语言处理（NLP）的采用率显著增加。最常见的人工智能任务是分类、检测和分割，不过在生成、数据集成和决策支持方面也有显著增长。文中介绍了牙科人工智能的分类框架。在数据模式中，基于文本的数据增长最为显著，同时传感器和信号数据的使用也有所增加。未来的研究应优先开发自然语言处理和混合人工智能模型，在研究、教育和治疗计划方面开展原创性研究，并针对口腔修复学和牙髓病学的诊断阶段进行系统综述。
