AI-driven innovations for dental implant treatment planning: A systematic review
Clinical Snapshot
PICO Framework
| P — Population | Patients requiring dental implant treatment planning |
| I — Intervention | Artificial intelligence models for implant planning |
| C — Comparator | Conventional treatment planning methods or no comparator |
| O — Outcomes | Accuracy of anatomical structure identification/segmentation, technical assistance effectiveness, predictive accuracy |
Bottom Line
This systematic review demonstrates promising potential for AI in dental implant planning, with accuracy rates up to 99.1% for anatomical segmentation. However, the evidence base is limited by predominantly low-quality studies (71% of included studies) and significant heterogeneity in methodology and outcomes. While AI shows particular promise in automating anatomical structure identification and providing technical assistance for surgical planning, the wide variation in reported accuracy (66.4-99.1%) and lack of standardised evaluation metrics highlight the need for more rigorous research. The finding that AI requires at least 50 training images for acceptable accuracy (>70%) has practical implications for implementation. Current evidence is insufficient to recommend routine clinical adoption, and practitioners should await further high-quality validation studies before integrating these technologies into clinical workflows.
Key Findings
P Value: Not applicable for systematic review
Effect Size: Accuracy rates 66.4% to 99.1% for anatomical segmentation
Primary Outcome: Accuracy of AI models in anatomical segmentation and technical assistance
Nnt Or Sensitivity: 83% accuracy for stent marker localisation, 70% predictive accuracy with 50+ training images
Confidence Interval: Not reported
Clinical Application
Limited by need for standardised evaluation parameters and further validation studies Relevant to Australian dental practice but requires TGA approval for clinical AI applications and integration with existing CBCT imaging protocols Dental practitioners performing implant treatment planning, particularly oral surgeons and prosthodontists
Abstract
PURPOSE: This systematic review evaluates the effectiveness of artificial intelligence (AI) models in dental implant treatment planning, focusing on: 1) identification, detection, and segmentation of anatomical structures; 2) technical assistance during treatment planning; and 3) additional relevant applications. STUDY SELECTION: A literature search of PubMed/MEDLINE, Scopus, and Web of Science was conducted for studies published in English until July 31, 2024. The included studies explored AI applications in implant treatment planning, excluding expert opinions, guidelines, and protocols. Three reviewers independently assessed study quality using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies, resolving disagreements by consensus. RESULTS: Of the 28 included studies, four were high, four were medium, and 20 were low quality according to the JBI scale. Eighteen studies on anatomical segmentation have demonstrated AI models with accuracy rates ranging from 66.4% to 99.1%. Eight studies examined AI's role in technical assistance for surgical planning, demonstrating its potential in predicting jawbone mineral density, optimizing drilling protocols, and classifying plans for maxillary sinus augmentation. One study indicated a learning curve for AI in implant planning, recommending at least 50 images for over 70% predictive accuracy. Another study reported 83% accuracy in localizing stent markers for implant sites, suggesting additional imaging planes to address a 17% miss rate and 2.8% false positives. CONCLUSIONS: AI models exhibit potential for automating dental implant planning with high accuracy in anatomical segmentation and insightful technical assistance. However, further well-designed studies with standardized evaluation parameters are required for pragmatic integration into clinical settings.
References
- 1.Zaww, K., Abbas, H., Vanegas Sáenz, J. R., & Hong, G. (2026). AI-driven innovations for dental implant treatment planning: A systematic review. Journal of Prosthodontic Research. https://doi.org/10.2186/jpr.JPR_D_24_00338
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