The Role of Artificial Intelligence in Predicting Dental Implant Success a Systemic Review

Abstract

Background: Dental implant design and diagnostics could be much improved by including artificial intelligence (AI) into dentistry. Not intended to replace dental practitioners, artificial intelligence systems provide second-informed opinions based on forecasts and quantitative decisions. Artificial intelligence nowadays has strong and accurate approaches for the diagnosis of dental diseases; nonetheless, little study in this sector highlights the need of more research and development of future prospects. Purpose: the aim of this systemic renew to assess artificial intelligence's contribution in dental implant success prediction. Methodology: Seventeen studies satisfied all inclusion criteria and were incorporated into the final evaluation. The stringent selection approach reduced the initial 408 records to only 4.2% qualifying research, facilitating a concentrated analysis. Results: Findings demonstrate AI's transformative potential across implantology, including diagnostic accuracy (AUC up to 0.954 for automated deep learning models), treatment planning alignment with clinical judgments, and optimization of biomaterials (e.g., 62.5% porosity titanium foam for stress shielding reduction). AI applications�ranging from convolutional neural networks (CNNs) for implant classification to real-time surgical navigation� significantly enhanced precision, with RCTs reporting higher success rates (92% vs. 78%). Conclusion: The integration of artificial intelligence (AI) integration into dental implantology improves diagnosis precision, treatment planning, surgical accuracy, and postoperative monitoring. Limitations: Larger, different datasets, better model generalizability, and validation in real-world clinical environments are among common issues. To get general acceptance and maximize its influence on dental treatment, artificial intelligence will need addressing these constraints