Artificial intelligence in implant dentistry: diagnostic performance, prediction, and design optimization

Artificial intelligence has emerged as a valuable tool in dental implantology, with applications focused on implant type recognition, the prediction of clinical success, and design optimization through integration with biomechanical models. Systems based on machine learning and deep learning enable the analysis of radiographic images and clinical variables to generate results that support decision-making. In the case of implant type recognition, the accuracy levels of the evaluated models ranged between 93.8% and 98%, suggesting high potential for classification tasks under controlled conditions. However, this accuracy is limited by the use of restricted datasets, given that more than 2,000 types of implants are available on the market. In predicting implant success, the results were more variable, with values ranging from 62.4% to 80.5%, reflecting the biological and clinical complexity of this type of prognosis. In terms of design, the combination of artificial intelligence with finite element analysis resulted in improvements, such as a 36.6% reduction in stress at the implant–bone interface, although these results are based on computational models without direct clinical validation. At the methodological level, relevant limitations were identified, including heterogeneity in outcome measures, lack of clarity in eligibility criteria, and a high risk of bias in some of the analyzed studies. Overall, the evidence suggests that artificial intelligence has significant potential in dental implantology, although its clinical application still requires further validation and methodological standardization.