Evidence

Accurate mandibular canal (MC) identification on cone-beam computed tomography (CBCT) is vital to prevent inferior alveolar nerve injury during oral and maxillofacial procedures. Manual segmentation is time-consuming and operator-dependent, while artificial intelligence (AI) offers automated, reproducible alternatives. This study compared the accuracy of automated MC segmentation across five AI platforms using standardized quantitative and qualitative evaluations.

To assess the clinical validity of artificial intelligence (AI)‐driven virtual implant placement compared to human intelligence (HI)‐based virtual and actual single implant placement in the posterior mandible.

Accurately registering intraoral and cone beam computed tomography (CBCT) scans in patients with metal artifacts poses a significant challenge. Whether a cloud-based platform trained for artificial intelligence (AI)-driven segmentation can improve registration is unclear. PURPOSE: The purpose of this clinical study was to validate a cloud-based platform trained for the AI-driven segmentation of prosthetic crowns on CBCT scans and subsequent multimodal intraoral scan-to-CBCT registration in the presence of high metal artifact expression.

Objective to validate an innovative artificial intelligence (AI)-driven tool for automated virtual implant placement by comparing its accuracy, implant dimension selection, time efficiency, and consistency with a human intelligence (HI)-based approach for single posterior tooth replacement.

The purpose of this clinical study was to validate a cloud-based platform trained for the AI-driven segmentation of prosthetic crowns on CBCT scans and subsequent multimodal intraoral scan-to-CBCT registration in the presence of high metal artifact expression.

To assess quality, clinical acceptance, time-efficiency, and consistency of a novel artificial intelligence (AI)-driven tool for automated presurgical implant planning for single tooth replacement, compared to a human intelligence (HI)-based approach.