Vol. 11, Issue 3, Part D (2025)

The integration of Artificial Intelligence (AI) into implant dentistry represents a significant advancement in diagnostic, planning, and surgical procedures. This review aims to evaluate the accuracy of AI-based systems in various aspects of implant dentistry, including diagnosis, treatment planning, implant placement, and post-operative evaluation. AI models, particularly those utilizing machine learning and deep learning algorithms have demonstrated high levels of precision and reliability when interpreting radiographic images such as CBCT, panoramic radiographs, and intraoral scans. Various performance metrics including accuracy, sensitivity, specificity, and area under the curve (AUC) are employed to validate AI tools against human expert benchmarks. Studies report AI accuracy rates comparable to or even surpassing experienced clinicians in identifying anatomical landmarks, detecting pathologies, and planning implant positions. However, factors such as dataset quality, model architecture, and external validation significantly influence these outcomes. Despite promising results, challenges remain regarding generalizability, clinical integration, and ethical considerations. Continuous validation and standardization are essential for the safe and effective deployment of AI in implant dentistry.