Enterprise AI Analysis
Artificial intelligence (AI) in restorative dentistry: current trends and future prospects
By Mariya Najeeb and Shahid Islam
Artificial intelligence (AI) holds immense potential in revolutionizing restorative dentistry, offering transformative solutions for diagnostic, prognostic, and treatment planning tasks. Traditional restorative dentistry faces challenges such as clinical variability, resource limitations, and the need for data-driven diagnostic accuracy. AI's ability to address these issues by providing consistent, precise, and data-driven solutions is gaining significant attention. This comprehensive literature review explores AI applications in caries detection, endodontics, dental restorations, tooth surface loss, tooth shade determination, and regenerative dentistry. While this review focuses on restorative dentistry, AI's transformative impact extends to orthodontics, prosthodontics, implantology, and dental biomaterials, showcasing its versatility across various dental specialties. Emerging trends such as AI-powered robotic systems, virtual assistants, and multi-modal data integration are paving the way for groundbreaking innovations in restorative dentistry.
Executive Impact & Key Findings
This comprehensive review highlights the transformative potential of AI in restorative dentistry, addressing key challenges and outlining future prospects. AI significantly enhances diagnostic accuracy, treatment planning, and overall patient outcomes. Key advancements include high-precision caries detection (up to 95.21% accuracy), efficient tooth segmentation (99% for various structures), and improved endodontic diagnostics (92.75% for apical lesion detection). Beyond direct restorative procedures, AI's influence extends to orthodontics, prosthodontics, implantology, and biomaterials research, offering personalized solutions and streamlined workflows. The integration of AI-powered robotic systems and virtual assistants represents a significant future trend, promising further automation and enhanced patient engagement. While challenges such as data privacy, algorithmic bias, and the need for standardized training persist, collaborative efforts and continuous research are crucial for realizing AI's full potential in modern dental practice, paving the way for precision-driven, patient-centric dental care.
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Peak Diagnostic Accuracy
Achieved in implant detection using R-CNN on panoramic images [37].
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Caries Detection Accuracy
Highest accuracy for caries detection using CNN on OCT images [26].
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Efficiency Gain in Segmentation
Reduction in root canal segmentation time (AI vs. manual) [66].
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Studies Analyzed
Number of peer-reviewed studies published between 2020-2025(January).
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Caries Detection
Dental Restorations
Endodontics
Pedodontics
Tooth Surface Loss & Shade
Challenges & Future
AI, primarily through Convolutional Neural Networks (CNNs), significantly enhances caries detection. Studies show high accuracy (up to 95.21%) in identifying non-cavitated and interproximal lesions using intraoral, bitewing, OCT, and panoramic images. This enables earlier, more precise diagnosis and supports non-invasive treatments, benefiting both dentists and patients. Predictive models also estimate caries risk for specific tooth types and age groups.
AI revolutionizes the detection and planning of dental restorations, including veneers, inlays, onlays, composite resin, gold, amalgam, crowns, and bridges. CNN models achieve high accuracy in detecting existing restorations (up to 100% in specific controlled conditions). AI is also integral to CAD-CAM systems, analyzing intraoral 3D scans to craft precise digital models for various restorations, and aiding in accurate implant placement (up to 99.89% accuracy for implant detection).
AI serves as a critical diagnostic tool in endodontics, leveraging Artificial Neural Networks (ANNs) and deep learning for precise analysis. It assists in locating apical foramen, determining working length, identifying root fractures, periapical lesions (92.75% accuracy), and unique canal configurations. AI predicts post-operative pain after RCT (95.60% accuracy) and automates root canal segmentation (Dice similarity coefficients of 89–93%), significantly streamlining workflows and enhancing diagnostic confidence.
In pedodontics, AI supports customized orthodontic appliances, AI-enabled pain control, and integrates 4D goggles for behavior modification. Specifically, AI aids in early childhood caries detection and classification. Using methods like Ensemble of Classifier Chains and various machine learning models on clinical, demographic, and parental oral health data, AI achieves accuracy ranges of 58-67% in classifying early childhood caries, and up to 24.5% in predicting it from survey data, highlighting the potential for integrating diverse data sources.
AI, particularly ANNs, predicts Tooth Surface Loss (TSL) by analyzing contributing factors and proposes personalized preventive treatment plans. In tooth shade determination, CNNs extract spatial features from intraoral photographs, ensuring precise shade matching for restorations. BPNN and Fuzzy Logic contribute to adaptability across diverse datasets. These methods enhance accuracy and consistency of color reproduction in prosthetic restorations by recognizing subtle variations in teeth color. While promising, more research is needed specifically on AI for TSL and shade determination in the 2020-2023 timeframe.
Key challenges for AI in dentistry include the need for extensive and diverse datasets, data privacy concerns, algorithmic bias, and interpretability of AI decisions. Future research should focus on integrating AI with emerging technologies like 3D printing for personalized restorations, developing AI training programs for dental professionals, and exploring AI-powered robotic systems for automated procedures. Collaborative efforts are essential to overcome these limitations and maximize AI's transformative potential in clinical practice.
Systematic Review Methodology
The PRISMA flowchart illustrates the systematic approach used to identify, screen, and select studies for this comprehensive review on AI in restorative dentistry.
Identification (248 records)
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Duplication Check (0 removed)
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Screening (185 excluded)
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Eligibility (0 excluded)
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Final Selection (63 included)
- Records Identified: 248 (Includes 5 new studies)
- Duplicates Removed: 0
- Records Screened: 248
- Screening Exclusions: 185 (Not relevant: 100, No AI: 60, Published outside 2020-2024: 25)
- Full-text Articles Assessed: 63
- Eligibility Exclusions: 0
- Studies Included: 63
AI in Caries Detection: A Precision Leap
Studies demonstrated AI's ability to achieve remarkably high accuracy in detecting caries, particularly using advanced deep learning models like CNNs on OCT images.
95.21% Accuracy in Caries Detection (OCT)Source: [26]
| Feature | AI Benefits | Traditional Limitations |
|---|---|---|
| Diagnostic Speed |
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| Accuracy (Specific Tasks) |
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| Consistency |
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Source: [12], [16]
Automated Segmentation & Classification in Endodontics
AI-powered tools significantly enhance endodontic workflows by accurately segmenting root canals and detecting apical lesions, drastically reducing manual effort and improving diagnostic precision.
- Root Canal Segmentation (DSC): 89-93%
- Apical Lesion Detection Accuracy: 92.75%
Impact: Streamlines endodontic workflows, reduces segmentation time from ~2262s to ~42s, and offers robust diagnostic support for complex cases.
Source: [43], [66]
Calculate Your Potential AI ROI
Our AI solutions significantly reduce diagnostic and treatment planning time, leading to substantial cost savings and improved patient throughput in restorative dentistry. Optimize your practice with AI-driven precision.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A strategic, phased approach ensures successful integration and maximum impact of AI in your restorative dentistry practice. We guide you through every step.
Phase 1: Needs Assessment & Data Preparation
Duration: 1-2 Months
Identify specific areas for AI integration, assess existing infrastructure, and begin curating and digitizing relevant patient data in a standardized format.
Phase 2: Pilot Program & Model Training
Duration: 3-6 Months
Implement a pilot AI solution (e.g., caries detection, segmentation) using prepared datasets. Train and fine-tune AI models, focusing on accuracy and clinical relevance. Establish initial performance benchmarks.
Phase 3: Integration & Clinician Training
Duration: 6-12 Months
Integrate AI tools into existing dental workflows. Provide comprehensive training for dental professionals on using AI systems, interpreting results, and ethical considerations. Conduct iterative feedback sessions.
Phase 4: Scaling & Continuous Optimization
Duration: 12+ Months
Expand AI deployment across the practice. Continuously monitor model performance, update with new data, and explore advanced AI applications like predictive analytics and robotic assistance. Ensure compliance and address emerging challenges.
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