Artificial intelligence (AI) applications are growing in dental implant procedures. For example, various AI models have been developed to recognize implant types using periapical and panoramic radiographs. Similarly, AI applications have been developed to predict osteointegration success or implant prognosis using client risk factors and ontology criteria. Finite element analysis (FEA) and AI models have been combined to optimize dental implant designs. However, the expansion and performance of AI models in implant dentistry applications have not yet been systematically documented and analyzed.
This systematic review analyzed the performance of AI models in implant dentistry for implant type recognition, implant success prediction, and implant design optimization. Seventeen articles were included in the review: seven studies analyzed AI models for implant type recognition, seven studies included AI prediction models for implant success forecast, and three studies evaluated AI models for optimization of implant designs.
The authors concluded AI models for implant type recognition, implant success prediction, and implant design optimization have demonstrated great potential but are still in development. Additional studies are required to further develop and assess clinical performance of AI models for implant dentistry applications before recommending them for clinical practice.
