Artificial Intelligence-Supported Digital Implant Planning: A Literature-Based Framework for Assessing Accuracy and Workflow Efficiency

2026 07 23

​Artificial Intelligence-Supported Digital Implant Planning: A Literature-Based Framework for Assessing Accuracy and Workflow Efficiency

Autor: Natalie Faith Cheng, Dental Student, 

Dental Student, Institute of Dentistry, Queen Mary University of London, United Kingdom

Supervisor: Ali Nankali​

Clinical Reader in Digital Dentistry, Institute of Dentistry, Queen Mary University of London, United Kingdom

​

​

Abstract

Conventional digital implant planning is vulnerable to operator-dependent variability and time burden, particularly during segmentation and multimodal registration. Artificial intelligence (AI) has therefore been introduced to automate and assist these upstream steps with the goal of improving reproducibility and workflow efficiency. This review compares conventional and AI-supported planning across segmentation performance, implant positioning accuracy, workflow efficiency and clinical decision-making. Current evidence indicates that AI-supported workflows can improve the consistency of anatomical models, reduce planning time and support more reproducible decision-making. However, improvements in planned-to-placed implant accuracy appear more modest and constrained by imaging quality, manufacturing tolerances and intra-operative factors. Overall, AI-supported planning is best understood as a decision-support adjunct rather than a replacement for clinician judgement. The available literature supports its value in improving workflow consistency and efficiency, while highlighting the need for cautious clinical interpretation due to heterogeneous outcome reporting and limited prospective evidence.

Keywords

Artificial Intelligence, Digital Dentistry, Prosthodontics, Clinical Decision Support, Dental Technology, Implantology, Diagnostic Tools, AI-supported planning, Digital Workflow.

​