Artificial Intelligence Versus Human Expertise in Implant Treatment Planning: A Retrospective Cone-Beam Computed Tomography-Based Comparative Study

Introduction Accurate implant treatment planning is essential to achieve optimal functional and esthetic outcomes in modern dentistry. With the increasing integration of digital technologies, artificial intelligence (AI) has emerged as a promising tool for enhancing precision, consistency, and efficiency in clinical decision-making. However, evidence comparing AI-assisted planning to conventional manual approaches remains limited. This study aimed to compare the accuracy, efficiency, and reliability of AI-assisted implant treatment planning with manual clinician-based assessment, using cone-beam computed tomography (CBCT) data. Materials and methods This retrospective cross-sectional study included 30 CBCT scans of patients who underwent dental implant placement. Each dataset was analyzed using AI-assisted planning with Simplant Pro (Materialise NV, Leuven, Belgium; distributed by Dentsply Sirona, Charlotte, North Carolina, USA) and manual assessment by a blinded clinician. The evaluated parameters included bone height, buccolingual width, angular deviation, linear deviation, planning time, and clinician confidence. Postsurgical radiographs were used as reference standards. All the statistical analyses were performed. Paired t-tests and Wilcoxon signed-rank tests were applied, with p<0.05 considered statistically significant. Results AI demonstrated slightly lower bone height (12.06 ± 1.62 mm vs 12.36 ± 1.78 mm) and width (7.18 ± 0.82 mm vs 7.55 ± 0.98 mm) compared to manual assessment (p<0.05). Significantly lower angular deviation (1.90 ± 0.83° vs 3.99 ± 1.09°​​​​​​​) and linear deviation (0.43 ± 0.15 mm vs 1.01 ± 0.38 mm) were observed with AI (p<0.0001). Planning time was significantly reduced (8.5 ± 2.1 vs 21.4 ± 5.7 minutes), and clinician confidence was higher with AI (82.1 ± 9.2 vs 68.9 ± 11.0; p<0.001). The intraclass correlation showed excellent agreement with the bone measurements (Intraclass Correlation Coefficient or ICC>0.80). Conclusion AI-assisted implant planning demonstrated superior accuracy, efficiency, and consistency compared to manual methods. It can serve as a reliable adjunct to enhance clinical decision making and optimize treatment outcomes. Clinician acceptance of nanorobotic interventions is scenario-dependent, with higher preference in precision-based and minimally invasive cases. However, concerns regarding safety and procedural control continue to limit broader adoption. Prior experience with advanced technologies significantly influences acceptance. Wider clinical use will depend on stronger evidence and improved training.