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Prosthesis
Special Issues
Shaping the Future: Artificial Intelligence in Prosthodontics and Prosthesis Innovation
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Shaping the Future: Artificial Intelligence in Prosthodontics and Prosthesis Innovation

A special issue of Prosthesis (ISSN 2673-1592). This special issue belongs to the section "Prosthodontics".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1818

Special Issue Editors

Dr. Marco Tallarico

E-Mail Website
Guest Editor
Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
Interests: dental implants; digital dentistry; prosthesis
Special Issues, Collections and Topics in MDPI journals
Dr. Ieva Gendviliene

E-Mail Website
Guest Editor
Faculty of Medicine, Institute of Odontology, Vilnius University, Zalgirio g. 117, LT- 08217 Vilnius, Lithuania
Interests: oral surgery; maxillofacial surgery; implant dentistry; maxillofacial injuries; osseointegration; bone regeneration

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the transformative role of artificial intelligence (AI) in prosthodontics, from routine clinical applications to cutting-edge prosthetic technologies. We invite researchers to contribute original studies, reviews, and case-based insights that highlight how AI is shaping diagnostics, treatment planning, and the design and fabrication of dental prostheses. With this collection, we seek to assess the current state of AI integration in prosthodontic workflows and envision future directions for research and clinical practice. AI-driven tools promise to enhance the accuracy, predictability, and personalization of treatments, ultimately improving outcomes and patient satisfaction. This Special Issue will serve as a platform to showcase innovations that advance both the science and art of prosthodontics, offering a more intelligent, efficient, and patient-centered approach to care.

The main purpose of this Special Issue is to analyzed the role of AI in medicine and dentistry helping future generations to understand its actual and potential role.

In this Special Issue, original research articles, including interesting case reports and reviews, are welcome. Research areas may include (but are not limited to) any prosthodontics rehabilitations using AI-based tools.

We look forward to receiving your contributions!

Dr. Marco Tallarico
Dr. Ieva Gendviliene
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Prosthesis is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • prosthesis
  • rehabilitation
  • digital

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Published Papers (1 paper)

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29 pages, 3326 KB  
Systematic Review
Artificial Intelligence for Color Prediction and Esthetic Design in CAD/CAM Ceramic Restorations: A Systematic Review and Meta-Analyses
by Carlos M. Ardila, Diana María Pulgarín-Medina, Eliana Pineda-Vélez and Anny M. Vivares-Builes
Prosthesis 2025, 7(6), 160; https://doi.org/10.3390/prosthesis7060160 - 4 Dec 2025
Viewed by 1279
Abstract
Background/Objectives: Artificial intelligence (AI) is increasingly embedded in CAD/CAM workflows to address persistent challenges in restorative dentistry, including unpredictable color outcomes and time-intensive crown design steps. Yet, evidence on its accuracy and efficiency remains fragmented across heterogeneous study designs and metrics. This [...] Read more.
Background/Objectives: Artificial intelligence (AI) is increasingly embedded in CAD/CAM workflows to address persistent challenges in restorative dentistry, including unpredictable color outcomes and time-intensive crown design steps. Yet, evidence on its accuracy and efficiency remains fragmented across heterogeneous study designs and metrics. This systematic review and meta-analyses aimed to evaluate the accuracy and performance of AI for color prediction and automated crown design in CAD/CAM ceramics. Methods: A systematic review with random-effects meta-analyses. The outcomes included design time, internal fit, finish-line accuracy, color-prediction acceptability using ΔE00 (AT00), morphology deviation, and occlusal and proximal contacts. Results: Fifteen studies met the inclusion criteria. The meta-analyses showed that AI-equipped CAD reduced crown design time compared to conventional CAD (MD −88.7 s; 95% CI −134.5 to −42.9; I2 = 72%). The internal fit showed a small advantage for AI (MD −17.1 µm; 95% CI −26.2 to −7.9; I2 = 90%). For finish-line identification, the pooled mean Hausdorff distance was ~0.35 mm (95% CI 0.316–0.382; I2 = 0%). For color prediction, the pooled proportion of predictions within each study’s prespecified acceptability threshold (AT00) was near-universal (0.996; 95% CI 0.988–0.999; I2 = 0%). Morphology and functional contacts were not pooled due to incompatible metrics and units. Narrative synthesis indicated AI performance comparable to, or favorable over, conventional/technician workflows in selected regions. Conclusions: AI for CAD/CAM dentistry shows practical promise, most clearly for design-time efficiency and with encouraging signals for internal fit, finish-line identification, and color-prediction acceptability under study thresholds. However, clinical translation should proceed cautiously. Full article
(This article belongs to the Special Issue Shaping the Future: Artificial Intelligence in Prosthodontics and Prosthesis Innovation)
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