Abstract

Dr. Annem Venkata Shalmeeja, *Dr. Divya Kadali, Dr Farouq Afsha Md, Dr. Reehana Pathan and Dr. Lokeswari Devi Donavalli

ABSTRACT

Objective: This systematic review evaluates the scope and performance of artificial intelligence (AI) applications in dental implantology, focusing on their roles in implant type recognition, surgical planning, peri-implantitis prediction, and implant design optimization. Methods: A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Cochrane Library for studies published between January 2020 and March 2024. Eligible studies included original research, clinical trials, and in vitro studies reporting on AI tools in implant dentistry. Data were extracted regarding study design, AI techniques, sample size, diagnostic or predictive accuracy, and outcome measures. Risk of bias was evaluated using the Joanna Briggs Institute (JBI) and Newcastle–Ottawa Scale (NOS) checklists. Results: Thirty-six studies met the inclusion criteria. AI models demonstrated promising results: implant type identification accuracies ranged from 89% to 98.5%; implant planning models improved anatomical segmentation and reduced diagnostic time; peri-implantitis prediction tools achieved up to 90% accuracy; and AI-assisted finite element analysis optimized implant designs by reducing bone stress by up to 36.6%. However, barriers such as heterogeneity in datasets, lack of external validation, algorithm transparency, and ethical concerns were noted. Conclusions: AI applications in implant dentistry exhibit strong potential across diagnosis, planning, prognosis, and design. However, broader clinical validation and implementation frameworks are essential for safe, standardized integration.