Related Experiment Video
Updated: Jun 11, 2026
Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
Published on: February 23, 2024
Artificial intelligence in dental implant identifications, planning accuracies, and success predictions: An umbrella
Ankita Mathur1, Vini Mehta2, Mahati Bhadania3
1Assistant Professor, Department of Dental Research Cell, Dr. D. Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pune, India.
Statement Of Problem:
Artificial intelligence (AI) applications and related secondary data in implant dentistry have been expanding. However, reviews of these applications differ in their focus, quality, and conclusions.
Purpose:
This umbrella review aimed to consolidate evidence from systematic reviews and meta-analyses evaluating the effectiveness of AI models in implant dentistry.
Material And Methods:
A comprehensive search was conducted in Science Direct, PubMed-MEDLINE, Scopus, and Embase databases from their inception until April 2025. The review included systematic reviews, with or without meta-analyses, that examined periapical, cone beam computed tomography (CBCT) or panoramic radiographs of patients eligible for dental implants. These reviews assessed the accuracy of the AI model in determining appropriate implant types, predicting implant osseointegration, and forecasting treatment success. An overlap analysis of the primary studies was also performed. The characteristics of AI interventions were analyzed separately to evaluate their influence on implant treatment planning. The quality of the included reviews was appraised using the AMSTAR 2 tool.
Results:
From 261 identified records, only 10 studies met the inclusion criteria. Collectively, these studies showed that the AI models, particularly convolutional neural networks (CNN), showed better accuracy of over 90% for treating dental implants and predicting their success, outperforming other AI models. Only 5 reviews reported on AI-driven predictions of osseointegration, underscoring its emerging potential in enhancing clinical decision-making. Deep learning models consistently performed better than traditional machine learning models. Quality assessment using AMSTAR 2 indicated a moderate to high level of confidence.
Conclusions:
AI technologies, especially deep learning models, show the potential to enhance implant identifications, planning accuracies, and success predictions in implant dentistry. Nevertheless, challenges such as limited data sets, lack of analyses involving multiple implants, and issues related to standardization remain key barriers to broader clinical adoption.
