The combination of artificial intelligence (AI) and computer-aided design and manufacturing (CAD/CAM) technologies is changing dental implantology through the ability to plan based on the data, automatically segment the radiographic images, and translate the virtual plan to the clinical domain accurately. This is a literature review that summarizes modern research on the utilization of AI algorithms convolutional neural networks (CNNs), U-Net architectures and machine learning classifiers alongside CAD/CAM workflows to plan implants, design surgical guides, and place them with the use of robots. Our system search revealed 75 studies assessing level of segmentation accuracy, automated planning of treatment, fabrication of guides, navigation, clinical accuracy measures, and post-operative outcomes. The main results suggest that AI-enhanced planning helps to increase the constancy of the segmentation and decreases the time spent on planning by 85%, whereas the reduced linear deviations to values often less than 1.0 mm and angular deviations to within 240 degrees in most clinical groups can be achieved through integrated CAD/CAM and robotic planning. Difficulties identified are heterogeneity in datasets, CBCT imaging artifacts, regulation, interpretability of AI judgment, and robotics barriers to capital investment. Federated learning methods, explainable AI modules, standardized outcome reporting, and prospective multi-center validation are some of the recommendations to adopt in order to make AI more acceptable in clinics. This review aims at informing clinicians, researchers, and developers on how to achieve safe and efficient integration of AI and CAD/CAM in precision implant surgery.

Haoming Zhang. Integration of AI and CAD/CAM for Precision Implant Surgery. Frontiers in Medical Science Research (2026), Vol. 8, Issue 1: 63-71. https://doi.org/10.25236/FMSR.2026.080107.

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