Automation of Restoration Design: Comparison of AI-Assisted and Traditional Approaches in CAD/CAM Dentistry

Automation of Restoration Design: Comparison of AI-Assisted and Traditional Approaches in CAD/CAM Dentistry

Roman Shvets

Computational Intelligence and Machine Learning . 2025 April; 6(1): 59-63. Published online April 2025

doi.org/10.36647/CIML/06.01.A008

Abstract : The integration of artificial intelligence (AI) into CAD/CAM dentistry has revolutionized the workflow of dental restoration design, significantly enhancing efficiency and precision. AI-assisted systems, such as CEREC, leverage machine learning algorithms to generate dental restorations by analyzing extensive datasets of previously designed crowns, veneers, and bridges. This automated approach reduces the time required for restoration design while minimizing human errors. However, AI-generated restorations may lack the nuanced adjustments that experienced clinicians incorporate in manual digital design. Traditional manual CAD/CAM design remains a gold standard for complex cases requiring high esthetic and functional customization. In this approach, clinicians meticulously modify digital restorations based on patient-specific anatomy, occlusal patterns, and esthetic needs. While this method provides superior adaptability, it is time-intensive and highly dependent on the clinician’s expertise. This paper presents a comparative analysis of AI-assisted and traditional manual restoration design in CAD/CAM dentistry. We evaluate their advantages and limitations in terms of speed, precision, esthetic outcomes, and clinical applicability. AI-driven automation demonstrates remarkable efficiency in standard restorations, offering a predictable and reproducible workflow. Conversely, manual design provides greater flexibility, particularly in complex anterior cases requiring detailed morphology and occlusal adjustments. As AI technology continues to evolve, future advancements will focus on enhancing adaptive learning models, integrating AI-assisted occlusion analysis, and improving real-time human-AI collaboration. The optimal approach lies in balancing AI automation with clinician expertise to achieve restorations that are not only functional but also esthetically superior. This study underscores the importance of combining AI’s computational power with the artistic and diagnostic skills of dental professionals to ensure the best possible patient outcomes

Keyword : AI in dentistry, CAD/CAM, CEREC, Digital dentistry.