This repository contains research datasets derived from two complementary studies in dental radiology and implant dentistry. One dataset supports the development and validation of artificial intelligence–based methods for the automatic segmentation of the mandibular incisive canal in cone-beam computed tomography. It includes quantitative analysis metrics obtained for each case using a previous AI model, an enhanced AI model, and expert manual segmentation, as well as descriptive data of the training and validation samples. Time-efficiency measurements for all segmentation approaches are also provided. The other dataset originates from a scoping review investigating the application of artificial intelligence in the design of static surgical guides for dental implant placement. This dataset comprises extracted data from the included studies, including study characteristics, methodological features, and reported linear and angular implant deviation values.