Artificial Intelligence in Predicting Orthodontic Miniscrew Implant Success: A Comprehensive Review

Background In modern orthodontics, orthodontic miniscrew implants (MSIs) have become essential instruments for supplying transient skeletal anchoring. Success rates vary despite their extensive use and are impacted by a number of biological, biomechanical, and clinical factors. Because of the intricate interactions between these factors, predicting MSI success has historically been difficult.  Objective The purpose of this review is to present a thorough synthesis of the available data on the application of artificial intelligence (AI) to forecast the stability and success of orthodontic miniscrew implants.  Methods Using the PubMed, Scopus, Web of Science, and Google Scholar databases, a systematic literature review spanning research from 2005 to 2025 was carried out. Artificial intelligence, machine learning, deep learning, success rate, failure prediction, orthodontic miniscrew implant, and temporary anchorage device were among the search phrases used. With a focus on methodological approaches, predictive accuracy, and clinical translation, pertinent papers examining AI models for MSI outcome prediction were critically assessed.  Results When compared to traditional statistical methods, AI-based models such as artificial neural networks (ANNs), support vector machines (SVMs), random forest classifiers, and deep learning architectures showed superior predictive accuracy. The most significant predictors of success were cortical bone thickness, insertion torque, root proximity, and patient-related factors (age, sex, oral hygiene, and inflammation). The reported predictive accuracies of AI models ranged from 78% to 96%, outperforming clinician-based estimation and logistic regression.  Conclusion By offering precise, personalized forecasts of MSI success, artificial intelligence (AI) holds great promise for improving clinical decision-making in orthodontics. Even while recent research shows encouraging findings, widespread clinical integration won't happen until more validation in huge, multicenter, real-world clinical datasets.