Enterprise AI Analysis
Artificial intelligence in periodontal disease research: a bibliometric and visualized analysis of global research trends (2007–2025)
This bibliometric analysis explores the evolving landscape of AI in periodontal disease research from 2007 to 2025. It reveals a rapidly growing field, especially since 2021, with China and the United States leading in publications. Key contributors include Pusan National University and authors like Pradeep Kumar Yadalam. Core research focuses on 'periodontitis,' 'machine learning,' and 'artificial intelligence,' with 'progression' and 'expression' emerging as future trends. The study provides a systematic overview to guide dentists and researchers in understanding current applications and future clinical translation of AI in periodontology.
Key AI Impact Metrics
0
AI Relevance Score
healthcare
Relevant Industry
High
Business Opportunity
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Publication Trends
Key Contributors & Collaboration
Research Hotspots & Future Directions
Publication Trends
The research shows sustained growth in publications on AI and periodontal disease, particularly rapid acceleration since 2021. China leads in article output, while the US has the highest citation count, indicating significant influence.
Details: A total of 496 articles were analyzed, with annual publication output showing sustained growth, particularly since 2021. China contributed the most publications (153 articles), followed by the United States (72 articles), South Korea (30 articles), India (29 articles), and Germany (25 articles). In terms of citation counts, the United States leads with 1,959 citations. Key institutions include Pusan National University (32 articles) and Saveetha Institute of Medical and Technical Science (31 articles). BMC Oral Health published the most articles (23).
Key Contributors & Collaboration
The field exhibits strong collaboration, with a significant co-authorship network of 2,604 authors. Pradeep Kumar Yadalam is the most prolific author, and Orhan Kaan, Abu Patricia Angela R., and Falk Schwendicke are highly cited, indicating a mature and connected research community.
Details: The co-authorship network involves 2,604 authors, with an average of 5.25 authors per article. Pradeep Kumar Yadalam is the most prolific author with 15 articles, followed by Kaan Orhan (11 articles) and Falk Schwendicke (9 articles). Orhan Kaan, Abu Patricia Angela R., and Falk Schwendicke are identified as the most cited authors. International collaboration is notable, especially among the US, China, and Germany.
Research Hotspots & Future Directions
Current research focuses on 'periodontitis,' 'machine learning,' and 'artificial intelligence' for diagnosis and treatment. Emerging themes include 'progression' and 'expression,' pointing towards dynamic monitoring and in-depth mechanistic analysis.
Details: Keyword analysis reveals 'periodontitis,' 'machine learning,' and 'artificial intelligence' as core research foci. Burst detection highlights 'progression' and 'expression' as emerging thematic directions, indicating a shift towards dynamic monitoring (e.g., AI wearable monitoring) and deep mechanisms (gene expression analysis). Image-based diagnosis, particularly using panoramic radiographs and CBCT with CNNs, is a significant application area. Data-driven research for prediction and treatment effect evaluation is also growing.
0
Relevant Articles Analyzed
AI in Periodontal Research Workflow
Data Retrieval (WoSCC)
→
Data Processing (R, VOSviewer, CiteSpace)
→
Bibliometric Analysis (Trends, Networks)
→
Keyword Analysis (Hotspots, Bursts)
→
Identify Frontiers & Clinical Translation
| Aspect | Current Status/Challenges | Future Directions/Solutions |
|---|---|---|
| Diagnosis Field |
|
|
| Treatment Field |
|
|
| Mechanism Research |
|
|
Leading Research Institutions & Authors
The study identified key players in the field. Pusan National University (South Korea) leads in publications (32 articles), followed by Saveetha Institute of Medical and Technical Science (31 articles). Pradeep Kumar Yadalam is the most prolific author with 15 articles, and Falk Schwendicke has consistently published from 2007-2025. This highlights concentrated expertise crucial for future collaborations.
Outcome: These institutions and authors are at the forefront, driving innovation and shaping the future trajectory of AI applications in periodontology. Engaging with such leaders could accelerate enterprise AI adoption.
Quantify Your AI ROI Potential
Estimate the potential annual time savings and cost reductions AI could bring to your enterprise operations. Adjust the parameters to see a personalized projection.
Annual Cost Savings
$0
Hours Reclaimed Annually
0
Your AI Implementation Roadmap
A strategic overview of how AI can be integrated into your enterprise, designed for phased implementation and maximum impact based on the research.
Phase 1: AI Integration Assessment
Evaluate current periodontal diagnostic and treatment workflows. Identify specific areas where AI can provide immediate value, such as automated image analysis for bone loss or predictive modeling for disease progression. Conduct a feasibility study with existing data.
Phase 2: Pilot AI Deployment & Data Standardization
Implement a pilot AI solution in a controlled environment, focusing on a high-impact area like image-based diagnosis. Simultaneously, establish standardized data collection and annotation protocols to build a high-quality, privacy-compliant periodontal disease database. Explore federated learning for multi-center data integration.
Phase 3: Scaled AI Solutions & Interdisciplinary Collaboration
Expand AI deployment to cover more aspects of periodontal care, including personalized treatment planning and longitudinal monitoring. Foster interdisciplinary teams combining stomatology, AI, biology, and informatics to develop advanced solutions and explore deep mechanistic research (e.g., gene expression analysis). Develop regulatory frameworks for clinical AI products.
Phase 4: Continuous Innovation & Ethical Oversight
Integrate AI training into medical education curricula. Continuously monitor and update AI models for improved accuracy and generalizability. Implement robust ethical oversight for data privacy and algorithmic fairness to ensure sustainable and responsible AI development in periodontology.
Ready to Transform Your Enterprise with AI?
Connect with our AI strategists to discuss a tailored roadmap and unlock the full potential of artificial intelligence in your operations.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat