Novel AI-based automated virtual implant placement: Artificial versus human intelligence
Related Experiment Video
Updated: Jun 23, 2025
Dynamic Navigation for Dental Implant Placement
Published on: September 13, 2022
Bahaaeldeen M Elgarba1, Rocharles Cavalcante Fontenele2, Francesco Mangano3
1OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, Leuven 3000, Belgium; Department of Prosthodontics, Faculty of Dentistry, Tanta University, Tanta 31511, Egypt.
Objectives:
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.
Materials And Methods:
To validate a novel AI-driven implant placement tool, a dataset of 10 time-matching cone beam computed tomography (CBCT) scans and intra-oral scans (IOS) previously acquired for single mandibular molar/premolar implant placement was included. An AI pre-trained model for implant planning was compared to human expert-based planning, followed by the export, evaluation and comparison of two generic implants-AI-generated and human-generated-for each case. The quality of both approaches was assessed by 12 calibrated dentists through blinded observations using a visual analogue scale (VAS), while clinical acceptance was evaluated through an AI versus HI battle (Turing test). Subsequently, time efficiency and consistency were evaluated and compared between both planning methods.
Results:
Overall, 360 observations were gathered, with 240 dedicated to VAS, of which 95 % (AI) and 96 % (HI) required no major, clinically relevant corrections. In the AI versus HI Turing test (120 observations), 4 cases had matching judgments for AI and HI, with AI favoured in 3 and HI in 3. Additionally, AI completed planning more than twice as fast as HI, taking only 198 ± 33 s compared to 435 ± 92 s (p < 0.05). Furthermore, AI demonstrated higher consistency with zero-degree median surface deviation (MSD) compared to HI (MSD=0.3 ± 0.17 mm).
Conclusion:
AI demonstrated expert-quality and clinically acceptable single-implant planning, proving to be more time-efficient and consistent than the HI-based approach.
Clinical Significance:
Presurgical implant planning often requires multidisciplinary collaboration between highly experienced specialists, which can be complex, cumbersome and time-consuming. However, AI-driven implant planning has the potential to allow clinically acceptable planning, significantly more time-efficient and consistent than the human expert.
