Purpose: Smile design software increasingly relies on artificial intelligence (AI). However, using AI for smile design raises numerous technical and ethical concerns. This study aimed to evaluate these ethical issues.Methods: An international consortium of experts specialized in AI, dentistry, and smile design was engaged to emulate and assess the ethical challenges raised by the use of AI for smile design. An e‐Delphi protocol was used to seek the agreement of the ITU‐WHO group on well‐established ethical principles regarding the use of AI (wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency). Each principle included examples of ethical challenges that users might encounter when using AI for smile design.Results: On the first round of the e‐Delphi exercise, participants agreed that seven items should be considered in smile design (diversity, transparency, wellness, privacy protection, prudence, law and governance, and sustainable development), but the remaining four items (equity, accountability and responsibility, solidarity, and respect of autonomy) were rejected and had to be reformulated. After a second round, participants agreed to all items that should be considered while using AI for smile design.Conclusions: AI development and deployment for smile design should abide by the ethical principles of wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency.

Finite element (FE) models are widely used to investigate the biomechanics of reconstructed premolars. However, parameter identification is a complex step because experimental validation cannot always be conducted. The aim of this study was to collect the experimentally validated FE models of premolars, extract their parameters, and discuss trends. A systematic review was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Records were identified in three electronic databases (MEDLINE [PubMed], Scopus, The Cochrane Library) by two independent reviewers. Twenty-seven parameters dealing with failure criteria, model construction, material laws, boundary conditions, and model validation were extracted from the included articles. From 1306 records, 214 were selected for eligibility and entirely read. Among them, 19 studies were included. A heterogeneity was observed for several parameters associated with failure criteria and model construction. Elasticity, linearity, and isotropy were more often chosen for dental and periodontal tissues with a Young’s modulus mostly set at 18–18.6 GPa for dentine. Loading was mainly simulated by an axial force, and FE models were mostly validated by in vitro tests evaluating tooth strains, but different conditions about experiment type, sample size, and tooth status (intact or restored) were reported. In conclusion, material laws identified herein could be applied to future premolar FE models. However, further investigations such as sensitivity analysis are required for several parameters to clarify their indication.

This article reviews the applications of Finite Element Models (FEMs) in personalized dentistry, focusing on treatment planning, material selection, and CAD-CAM processes. It also discusses the challenges and future directions of using finite element analysis (FEA) in dental care. This study synthesizes current literature and case studies on FEMs in personalized dentistry, analyzing research articles, clinical reports, and technical papers on the application of FEA in dental biomechanics. Sources for this review include peer-reviewed journals, academic publications, clinical case studies, and technical papers on dental biomechanics and finite element analysis. Key databases such as PubMed, Scopus, Embase, and ArXiv were used to identify relevant studies. Studies were selected based on their relevance to the application of FEMs in personalized dentistry. Inclusion criteria were studies that discussed the use of FEA in treatment planning, material selection, and CAD-CAM processes in dentistry. Exclusion criteria included studies that did not focus on personalized dental treatments or did not utilize FEMs as a primary tool. FEMs are essential for personalized dentistry, offering a versatile platform for in-silico dental biomechanics modeling. They can help predict biomechanical behavior, optimize treatment outcomes, and minimize clinical complications. Despite needing further advancements, FEMs could help significantly enhance treatment precision and efficacy in personalized dental care. FEMs in personalized dentistry hold the potential to significantly improve treatment precision and efficacy, optimizing outcomes and reducing complications. Their integration underscores the need for interdisciplinary collaboration and advancements in computational techniques to enhance personalized dental care.

Expert determination of the third molar maturity index (I3M) constitutes one of the most common approaches for dental age estimation. This work aimed to investigate the technical feasibility of creating a decision-making tool based on I3M to support expert decision-making. Methods: The dataset consisted of 456 images from France and Uganda. Two deep learning approaches (Mask R-CNN, U-Net) were compared on mandibular radiographs, leading to a two-part instance segmentation (apical and coronal). Then, two topological data analysis approaches were compared on the inferred mask: one with a deep learning component (TDA-DL), one without (TDA). Regarding mask inference, U-Net had a better accuracy (mean intersection over union metric (mIoU)), 91.2% compared to 83.8% for Mask R-CNN. The combination of U-Net with TDA or TDA-DL to compute the I3M score revealed satisfying results in comparison with a dental forensic expert. The mean ± SD absolute error was 0.04 ± 0.03 for TDA, and 0.06 ± 0.04 for TDA-DL. The Pearson correlation coefficient of the I3M scores between the expert and a U-Net model was 0.93 when combined with TDA and 0.89 with TDA-DL. This pilot study illustrates the potential feasibility to automate an I3M solution combining a deep learning and a topological approach, with 95% accuracy in comparison with an expert.

Regenerative endodontic procedures (REPs) aim at recreating dental pulp tissue using biomaterials such as hydrogels. Their bioactivity is mostly related to the nature of biomolecules or chemical compounds that compose the endodontic hydrogel. However, many other parameters, such as hydrogel concentration, bioactive molecules solubility, and apex size, were reported to influence the reciprocal host–biomaterial relationship and hydrogel behavior. The lack of knowledge regarding these various parameters, which should be considered, leads to the inability to predict the clinical outcome and suggests that the biological activity of endodontic hydrogel is impossible to anticipate and could hinder the bench-to-bedside transition. We describe, in this review, that most of these parameters could be identified, described, and studied. A second part of the review lists some challenges and perspectives, including development of future mathematical models that are able to explain, and eventually predict, the bioactivity of endodontic hydrogel used in a clinical setting.