Predicting implant success: How Artificial intelligence models are transforming peri-implant bone loss assessment

Peri-implant bone loss significantly impacts the longevity and success of dental implants, which are pivotal in restoring oral function and enhancing the patient’s quality of life. Traditional methods for predicting implant success often rely on subjective assessments and static data, limiting their accuracy and early detection capabilities. This paper explores how artificial intelligence (AI) models are transforming the assessment of peri-implant bone loss and improving implant success predictions. By analyzing complex datasets—including clinical records, imaging, and genomic information—AI technologies such as machine learning and deep learning offer enhanced predictive accuracy. These models enable early detection of risk factors, personalized treatment plans, and greater efficiency in clinical workflows. We discuss the mechanisms of peri-implant bone loss, the limitations of conventional prediction methods, and the implementation of AI models in predictive modeling. While highlighting the advantages of AI, the paper also addresses challenges such as data privacy, technical limitations, and the need for clinical integration. Future perspectives include advancements in AI technologies, integration with other emerging technologies, regulatory efforts, and the importance of long-term clinical studies. The integration of AI into dental practice holds the potential to revolutionize implant care, leading to improved patient outcomes and a transformation in dental healthcare delivery.