Applications of Artificial Intelligence in Implant Dentistry: A Systematic Review

Abstract

Introduction: The integration of artificial intelligence (AI) in healthcare has significantly transformed various fields, including dentistry. This systematic review explores the applications of AI in implant dentistry, focusing on its impact on diagnosis, treatment planning, and post-treatment monitoring.

Methods: : An electronic literature search was carried out through PubMed, Google Scholar, K-HUB, Prosquest, and Cochrane Review  databases from the year 2005 to 2024 by two independent investigators. The comprehensive search was restricted to the studies published in the English language from the year of origin of the earliest studies to identify and collect evidence that answers the PICO questions.

Results: Twenty articles were included: 13 investigations analysed AI models for implant type recognition, 5 studies included AI prediction models for implant success forecast, 1 study evaluated AI models for optimization of implant designs using FEM analysis and 1 study analysed osseointegration.

Conclusions: This systematic review highlights the transformative role of artificial intelligence (AI), particularly deep learning and CNNs, in implant dentistry—enhancing diagnostic accuracy, treatment planning, and implant design with performance that often exceeds human expertise. While AI shows great promise, especially with the use of large imaging datasets like CBCT, ethical considerations such as data privacy, bias mitigation, and transparency are essential to ensure its responsible integration alongside clinical expertise.