Enterprise AI Analysis
Trends and Classification of Artificial Intelligence Models Utilized in Dentistry: A Bibliometric Study
Cureus 17(4): e81836, Published 04/07/2025
This study employs a novel evaluative approach to AI applications 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, identified 1,368 studies. Key findings include a continuous rise in AI-related dental research, with orthodontics and diagnostic applications being most common. Recent growth is significant in treatment planning, research and education, and the adoption of hybrid AI models and NLP. The study presents a classification framework for AI in dentistry and highlights future research priorities.
Key AI Integration Metrics & Impact
This research highlights critical areas for AI adoption and growth within the dental industry, indicating clear trends and future potentials for enterprise-level applications.
418
Articles Published in 2024
3911
Total Keywords Identified
242
Publications from China
71
Forecasted NLP Studies (2025)
58
Forecasted Hybrid DL Models (2025)
249
Forecasted Text-based Data Use (2025)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Of 2,810 retrieved records, 1,368 met inclusion criteria. Exclusions were due to lack of thematic relevance, non-true AI robotics, and non-English studies.
Search & Inclusion Process
Records identified from Web of Science Core Collection (n = 2810)
→
Records excluded (n = 1442)
→
Articles included (n = 1368)
→
Original studies (n = 1124)
→
Review studies (n = 244)
1368
Studies Met Inclusion Criteria (out of 2810)
Continuous rise in AI-related dental research. Number of primary and review studies became comparable to proof-of-concept studies in 2023-2024. 'Dentistry, oral surgery & medicine' and 'artificial intelligence' are frequent keywords. China, USA, and India are leading contributors.
418
Articles Published in 2024
3911
Total Keywords Identified
242
Publications from China
| Year | Keywords |
|---|---|
| 2020 |
|
| 2024 |
|
Orthodontics is the most studied discipline, followed by oral and maxillofacial radiology, research and education, and operative dentistry. AI is primarily applied in the diagnosis stage. Treatment planning and research/education show highest recent growth.
72
Forecasted Research & Education Studies (2025)
| Category | Estimated 2024 Activity | Forecasted 2025 Growth |
|---|---|---|
| Orthodontics | 57 studies | 65 studies |
| Research & Education | 58 studies | 72 studies (highest growth) |
| Diagnosis Stage | 59% of studies | 58% |
| Treatment Planning Stage | 20% of studies | 24% (significant growth) |
DL is dominant, but hybrid models and NLP are rapidly growing. Text-based data is the fastest-growing modality. Classification, detection, and segmentation are common tasks, with generation, data integration, and decision support showing notable increases.
71
Forecasted NLP Studies (2025)
58
Forecasted Hybrid DL Models (2025)
249
Forecasted Text-based Data Studies (2025)
249
Forecasted Text-based Data Studies (2025)
Hybrid AI in Action: Enhanced Diagnostics
Hybrid AI models combine multiple AI subsets, offering integrated solutions for complex tasks. For example, in prosthodontics, they integrate CNNs and GANs for designing prostheses while ensuring material selection and functional success. These models also enable interdisciplinary approaches, such as combining textual, radiographic, and histological data to deliver comprehensive diagnostic and treatment plans.
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Your Enterprise AI Implementation Roadmap
A strategic phased approach to integrating AI into your dental enterprise, maximizing efficiency and impact based on the latest research.
Phase 1: Discovery & Strategy
Conduct a detailed assessment of current workflows, identify AI opportunities, and define clear objectives and success metrics. Select core AI models based on dental discipline needs.
Phase 2: Pilot & Integration
Implement a pilot AI solution in a specific dental discipline (e.g., orthodontics or diagnostics) with hybrid models and textual data. Integrate with existing systems.
Phase 3: Scaling & Optimization
Expand AI solutions across multiple disciplines, focusing on treatment planning and research/education. Continuously monitor performance and refine models based on real-world data, prioritizing NLP and sensor data.
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