Abstract
The aim of this systematic review was to comprehensively analyse recent studies on the application of artificial intelligence (AI) in dental implantology. The PRISMA guidelines were followed. Five databases were accessed: Scopus, Web of Science, MEDLINE/PubMed, IEEE Xplore, and JSTOR. Documents published between 2018 and October 15, 2024 relating to AI and implantology were considered. Exclusions encompassed reviews, opinion articles, books, conference references, studies using AI as a supplementary method, AI for teaching implant dentistry, and AI for implant fabrication, prothesis, or design. A total of 120 relevant papers were included. Risk of bias was assessed using PROBAST. Findings demonstrated extensive utilization of AI in various aspects of dental implantology: guided surgery, diagnosis, classification of oral structures, bone classification, classification of dental restorations, implant classification, implant planning, and implant prognosis. Deep learning algorithms were employed in 89.2% of studies, predominantly utilizing image data (72.0% two-dimensional images and 28.0% three-dimensional images). Publications doubled in 2022 compared to the previous year and have remained consistent since. Despite growth, the field remains relatively underdeveloped. However, with advancements in technology and data quality, substantial progress is anticipated in forthcoming years. Remarkably, 11 studies were found to have a high risk of bias.
Ignacio Arganda-Carreras
Ikerbasque Research Associate Professor
My research interests include image processing, computer vision, and deep learning for biomedical applications.