AI models for predicting long-term success of dental implants in medically compromised patients

Abstract

Predicting the long-term success of dental implants in medically compromised patients remains a major challenge in implant dentistry due to the influence of multiple systemic and local risk factors. This retrospective study analyzed data from 300 medically compromised patients to develop and evaluate artificial intelligence (AI)-based predictive models for implant outcomes using demographic, systemic and implant-related variables. Machine learning models, particularly the random forest algorithm, demonstrated higher predictive accuracy than conventional statistical methods. Systemic conditions, smoking status and bone quality were identified as significant predictors of implant success. Thus, data shows the AI-based predictive models can facilitate personalized risk assessment and enhance clinical decision-making in implant dentistry.