In recent years, Artificial Intelligence (AI) has emerged as a disruptive force across all medical disciplines, and dental implantology is rapidly adopting this innovation.
As dental treatments become increasingly digitalized, AI offers advanced capabilities that enhance the precision, speed, and safety of implant planning and execution.
A comprehensive scoping review published in Bioengineering (2024) analyzed 56 peer-reviewed clinical studies, revealing that AI is already influencing various stages of dental implantology—particularly in the preoperative phase.
By automating tasks such as anatomical landmark identification, bone quality assessment, and radiographic interpretation, AI supports clinicians in making more accurate and personalized treatment decisions.
Moreover, the integration of deep learning techniques, especially convolutional neural networks (CNNs), allows AI systems to process and interpret CBCT images with remarkable accuracy.
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These advancements promise not only to optimize surgical outcomes but also to reduce complications and improve long-term implant success rates.
However, the review also notes that clinical implementation is still in its early stages.
Despite encouraging results, there remains a need for standardized evaluation protocols, larger datasets, and real-world clinical trials to validate AI’s reliability across diverse patient populations.
Main Findings of the Review
1. Preoperative Planning Dominates AI Use
Most of the AI applications identified are used during the preoperative phase, including:
✔ Anatomical landmark identification (e.g., mandibular canal, sinuses)
✔ Bone quality/volume analysis. These functions are primarily powered by Convolutional Neural Networks (CNNs) applied to CBCT imaging.
2. AI Models Focus on Radiographic Interpretation
Over 80% of the studies reviewed used AI to assist in analyzing radiographs. Key applications include:
✔ Detection of critical structures
✔ Measuring bone dimensions
✔ Implant position simulation
3. Diagnostic Accuracy is Promising
AI models demonstrated high sensitivity and specificity, with some CNN-based tools achieving over 90% accuracy in identifying key anatomical features — paving the way for more reliable, automated diagnostics in the future.
4. Lack of Standardization
Despite the promising results, the review noted:
✔ A lack of clinical validation
✔ No consensus on evaluation metrics
✔ High variability in dataset size and quality
These gaps highlight the need for standardized protocols and multicenter trials to ensure clinical applicability.
5. Limited Use in Intraoperative and Postoperative Stages
Only a small portion of studies explored AI for:
✔ Surgical navigation
✔ Postoperative assessment
This reveals a major opportunity for innovation in integrating AI throughout the full implant workflow.
What AI Techniques Are Being Used?
Technique
✔ CNNs (Deep Learning). Radiograph interpretation, anatomical mapping
✔ Machine Learning. Predictive modeling of implant outcomes
✔ 3D Modeling & Image Segmentation. Surgical planning, bone structure simulation
Limitations Mentioned
✔ Few prospective or randomized clinical trials
✔ Insufficient real-world validation
✔ Data imbalance and overfitting risks
Conclusion of the Authors
AI holds great promise in dental implantology, particularly in preoperative planning. However, clinical integration is still in its early stages, and further development is needed to:
✔ Validate tools in real clinical settings
✔ Expand applications beyond diagnostics
✔ Build ethical and regulatory frameworks
📚 Citation
