Artificial intelligence and finite element analysis: applications in implant dentistry

Abstract

Artificial intelligence (AI) has shown great potential across scientific disciplines, including implant dentistry. This review investigates the applications of AI in Finite Element Analysis (FEA) of dental implants, examining implications, limitations, and future directions. By following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, relevant articles were obtained from PubMed, Scopus, Web of Science, and Google Scholar databases. Six articles were included, covering topics such as osseointegration assessment, implant design optimization, and bone healing prediction. Integrating AI and FEA can improve parameter optimization, computational efficiency, and analysis time. FEA simulations were consistently used to train AI models, which were then validated against FEA-calculated data. While AI in dental implantology is still in its early stages, opportunities for innovation and refinement are apparent. Challenges, such as algorithmic misconduct and interpretation of AI outputs, need to be addressed through collaborative efforts between clinicians and computer engineers. Future research should explore incorporating factors like bone homeostasis and multiscale analysis to enhance understanding of peri-implant bone response. Long-term clinical studies are necessary to validate AI model predictions in real-world scenarios.