Artificial Intelligence in Dental Implant Prognosis: A Narrative Review of Predictive Models, Performance, and Clinical Potential

Dental implants redefined present dentistry, presenting a resilient and offering a durable and efficient method for restoring lost teeth. The efficacy of as a therapeutic modality for the restoration of lost teeth has progressively advanced. Nevertheless, compromised osseointegration in the initial healing stage remains the major cause of early implant failure after surgery. The rising prevalence of implant treatment indicates that numerous patients may face complications, affecting both patient wellbeing and healthcare costs. As a result, predictive models for implant prognosis represent an immediate necessity, especially those designed to detect high-risk cases in the early stages to support intervention. This review summarizes recent data on the use of AI in prognosis of dental implant, underscoring datasets, methodologies, performance metrics and key models, Studies show that AI, especially hybrid and CNN models merging patient data with imaging with patient data, can yield strong accuracy in forecasting implant success or failure. Benefits consist of early identification of implants at risk, standardizing diagnostic evaluation, and strengthened evidence-based treatment planning. Nevertheless, challenges persist in areas such as clinical integration, ethical considerations, data quality and model. Future studies should emphasize multi-center and large studies, standardized assessment protocols and multimodal artificial intelligence approaches