International Journal of Chemical and Biochemical Sciences (ISSN 2226-9614)[/vc_column_text][/vc_column][/vc_row]
VOLUME 29(24) (2026)
Artificial Intelligence–Based Versus Conventional Computer-Aided Design Software for Full and Partial Coverage Restorations: A Narrative Review
Hania Sleem1, Dina Ahmed2
1Fixed Prosthodontics Department, Misr International University, Cairo, Egypt
2Dina Ahmed, Endodontics Department, St Petersburg University in Cairo, Egypt
Abstract
Computer-aided design and computer-aided manufacturing (CAD/CAM) technology has fundamentally transformed fixed prosthodontics by improving precision, efficiency, and reproducibility of indirect restorations. Conventional computer-aided design (CAD) software has demonstrated predictable outcomes for full- and partial-coverage restorations; however, restoration quality remains influenced by operator experience and manual design decisions. Recently, artificial intelligence (AI) has emerged as a promising advancement in digital restorative dentistry by incorporating machine learning and deep learning algorithms into CAD workflows to automate restoration design and improve efficiency. The aim of this narrative review was to summarize and critically evaluate current evidence comparing AI-based and conventional CAD software for designing full- and partial-coverage restorations. The available literature on CAD/CAM workflows, conventional and AI-driven design platforms, restoration accuracy, marginal adaptation, internal fit, occlusal morphology, and restorative materials, was reviewed and analyzed. Current evidence indicates that both conventional and AI-assisted CAD systems can produce restorations with clinically acceptable marginal adaptation and internal fit. AI-based software consistently reduces design time and enhances workflow efficiency while demonstrating accuracy comparable to conventional CAD systems for single-unit restorations. Nevertheless, Conventional CAD software operated by experienced clinicians or dental technicians generally remains superior for reproducing complex occlusal morphology and individualized proximal contact relationships. Restoration accuracy is influenced by restoration type, preparation design, restorative material, scanning protocol, software algorithm, and manufacturing technique. Although AI substantially improves efficiency and standardization, clinician oversight and final design refinement remain essential for optimal prosthetic outcomes.
Keywords: Artificial intelligence; Computer-aided design; Fixed prosthodontics; Full-coverage restorations; Partial-coverage restorations
Review article *Corresponding Author, e-mail: mohamedelboghdady120@gmail.com; Doi # https://doi.org/10.62877/1-IJCBS-26-29-24-1
Submitted: 02-08-2026; Accepted: 12-09-2026; Published: 12-09-2026