Artificial Intelligence in Dental Implant Identification: A Comprehensive Overview
DOI:
https://doi.org/10.9734/bpi/msti/v7/4409Keywords:
Artificial intelligence, dental implants, implant identification, radiographic analysis, deep learning, treatment planning, AI tools, diagnostic accuracy, implant database, 3D imagingAbstract
Background: Dental implantology has significantly transformed the field of restorative dentistry, providing patients with long-term, functional, and aesthetic solutions for missing teeth. As the demand for implants increases globally, the need for effective and accurate implant fixture identification has become more crucial.
Aim: This review aims to explore the role of artificial intelligence (AI) in dental implant identification, focusing on its applications, benefits, and challenges in clinical practice. The study examines AI-driven tools and their impact on diagnostic accuracy, clinical decision-making, and treatment planning.
Methodology: A comprehensive literature review was conducted using Medline (PubMed) and Google Scholar databases in January 2025. The search targeted studies and reviews on AI applications in dental implant identification, analyzing technological advancements and their clinical implications. A total of 28 relevant articles were selected for assessment.
Results: AI-powered tools, such as Spotimplant.com, Implantif.ai, and AI2D, have demonstrated high accuracy in identifying dental implants from radiographic images. Studies have shown that AI-based systems can improve identification precision by up to 25% compared to traditional methods. These technologies streamline the identification process, reduce human error, and enhance treatment planning. However, challenges remain, including database limitations, difficulties in complex cases, and the need for regulatory compliance.
Conclusion: AI-driven implant identification offers significant advantages in improving diagnostic accuracy and clinical efficiency. While current AI tools present challenges related to data quality, regulatory frameworks, and integration into clinical workflows, ongoing advancements are expected to enhance their reliability and applicability. Future research should focus on expanding AI training datasets, optimizing deep learning models, and integrating AI into digital dental workflows for personalized treatment planning.
