Artificial intelligence and smile design: An e-Delphi consensus statement of ethical challenges.

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.

Concordance of judgement: A tool to foster the development of professionalism in dentistry.

INTRODUCTION: Developing professionalism notably involves learning how to make professional judgements in ambiguous situations. The Concordance of Judgement Test (CJT) is a learning tool that was proposed to develop professionalism competencies, but it was never performed in dentistry or used with a synchronous methodology. The present study evaluated the feasibility of the use of CJT in the context of dental education, to foster professionalism and stimulate reflexivity and discussion. MATERIALS AND METHODS: After different steps of optimization, a questionnaire presenting 12 vignettes was submitted to 33 Canadian students. Second, after an additional optimization, a questionnaire of 7 vignettes was submitted to 87 French students. An immediate educational feedback was proposed after each vignette to promote reflexivity and discussions during the experience. RESULTS: The overall experience of the students was reported as good, thanks to the feedback of real-life situations. This promoted reflexivity and stimulated discussion between students and educators regarding professionalism issues. The students considered CJT as a relevant and well-adapted tool, and reported positive feelings regarding the inter-university aspect of the activity. The mean score of the panel members was close to 80/100 and the mean score of the students was 5 to 10 points lower, which is in agreement with docimological performance. CONCLUSION: The results suggested that the use of CJT in a synchronous way was a feasible and relevant tool to motivate the students to improve their professionalism, and to stimulate their reflexivity and discussion. The students reported positive experience with CJT, and we believe that this tool can be integrated in the dental curriculum.

Developing Advanced Patient-Specific In Silico Models: A New Era in Biomechanical Analysis of Tooth Autotransplantation.

INTRODUCTION: As personalized medicine advances, there is an escalating need for sophisticated tools to understand complex biomechanical phenomena in clinical research. Recognizing a significant gap, this study pioneers the development of patient-specific in silico models for tooth autotransplantation (TAT), setting a new standard for predictive accuracy and reliability in evaluating TAT outcomes. METHODS: Development of the models relied on 6 consecutive cases of young patients (mean age 11.66 years ± 0.79), all undergoing TAT procedures. The development process involved creating detailed in silico replicas of patient oral structures, focusing on transplanting upper premolars to central incisors. These models underpinned finite element analysis simulations, testing various masticatory and traumatic scenarios. RESULTS: The models highlighted critical biomechanical insights. The finite element models indicated homogeneous stress distribution in control teeth, contrasted by shape-dependent stress patterns in transplanted teeth. The surface deviation in the postoperative year for the transplanted elements showed a mean deviation of 0.33 mm (±0.28), significantly higher than their contralateral counterparts at 0.05 mm (±0.04). CONCLUSIONS: By developing advanced patient-specific in silico models, we are ushering in a transformative era in TAT research and practice. These models are not just analytical tools; they are predictive instruments capturing patient uniqueness, including anatomical, masticatory, and tissue variables, essential for understanding biomechanical responses in TAT. This foundational work paves the way for future studies, where applying these models to larger cohorts will further validate their predictive capabilities and influence on TAT success parameters.

[The challenges of artificial intelligence in odontology]. / Les enjeux de l'intelligence artificielle en odontologie.

Artificial intelligence has numerous potential applications in dentistry, as these algorithms aim to improve the efficiency and safety of several clinical situations. While the first commercial solutions are being proposed, most of these algorithms have not been sufficiently validated for clinical use. This article describes the challenges surrounding the development of these new tools, to help clinicians to keep a critical eye on this technology.

Title: Les enjeux de l'intelligence artificielle en odontologie. Abstract: Les applications potentielles de l'intelligence artificielle, ces algorithmes visant à améliorer l'efficacité et la sécurité de diverses décisions cliniques, sont nombreuses en odontologie. Alors que les premiers logiciels commerciaux commencent à être proposés, la plupart des algorithmes n'ont pas été solidement validés pour une utilisation clinique. Cet article décrit les enjeux entourant le développement de ces nouveaux outils, afin d'aider les praticiens à garder un regard éclairé et critique sur cette nouvelle approche.

Investigation of the early apical release from endodontic hydrogels: A 3D printed model.

AIM: Regenerative Endodontic Procedures (REPs) using new materials such as hydrogels aim to replace current endodontic treatments, but numerous limitations are to overcome. Apical release was little explored in previous studies, especially regarding hydrogels that incorporate molecules, such as growth factors and antibiotics. Apical release is a key mechanism in achieving regeneration, as it could regulate disinfection or cell colonization. Few models exist for apical release, limiting the transfer of these devices from bench to bedside. This study aims to design a simple and standardized model to identify parameters that influence the early apical release kinetic of molecules from endodontic hydrogels. METHODOLOGY: Endodontic Release Inserts (ERI) were designed to mimic the situation of an immature incisor using three different diameters (Ø 0.5 to 2 mm) and to allow the study of the early release from a hydrogel in a 96-well plate. ERI was produced with a 3D printing machine. The kinetic release was investigated using 2 fluorescent, hydrophobic (BDP-500) and hydrophilic (Fluorescein) molecules, in different hydrogels (fibrin and agarose) and in various media (PBS or serum). The release kinetics were estimated by measuring the fluorescence at different time points (1 to 24 h). RESULTS: ERI use made it possible to report that apical diameters increase from 500 to 1000 µm was associated with an increase in release from 4.02 ± 1.63% to 11.53 ± 2.38% over 24 h. It also allowed us to report that bottom solution composition change from PBS to human serum was associated with an increase in the release of fatty acid molecules, whilst a decrease in the hydrogel concentration was associated with a variation in release kinetics. Moreover, nano-encapsulation of a molecule was associated with a decreased release over the first 24 h from 5.25 to 0%. CONCLUSION: ERI use enables investigation of the parameters influencing release kinetics from endodontic hydrogels. Further investigations are necessary to evaluate the interaction of these parameters with each other, in animal models and clinic.

Bioactive Endodontic Hydrogels: From Parameters to Personalized Medicine.

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.

Oral Health of Rural Cameroonian Children: A Pilot Study in Bamendou.

Access to dental care in Cameroon is a public health issue, particularly for children living in rural areas. Given the lack of recent data, the investigation of children's oral health along with their oral hygiene behavior, needs in terms of care, and access to oral hygiene materials were investigated. This cross-sectional pilot study was conducted in Bamendou, Western Region of Cameroon. The study population included 265 children aged 3-18 years who completed a questionnaire about their oral hygiene practices. A clinical examination assessed dental caries, calculus, gingivitis, and oral hygiene. The Chi-squared test was used to identify potential factors influencing caries prevalence rates (significance threshold: p < 0.05). Among the 265 children (females: 41.5%, mean age 9.3 years), caries prevalence (ICDAS ≥ 2) was 78.5% and significantly increased with age: 62.2% (3-6 years), 80.9% (7-11 years) and 84.1% (12-18 years, p = 0.01). Virtually no children (95.1%) had ever visited a dentist. While only 23.4% of children brushed their teeth at least twice a day, 14% worryingly reported the use of products other than toothpaste (ash, soap, salt, or bicarbonate) and 13.6% no brushing product. The present study revealed a high prevalence of dental caries in this population and inadequate toothbrushing habits, which highlights the need for preventive oral health education and intervention to address these issues.

Real-time simulation of the transplanted tooth using model order reduction.

The biomechanics of transplanted teeth remain poorly understood due to a lack of models. In this context, finite element (FE) analysis has been used to evaluate the influence of occlusal morphology and root form on the biomechanical behavior of the transplanted tooth, but the construction of a FE model is extremely time-consuming. Model order reduction (MOR) techniques have been used in the medical field to reduce computing time, and the present study aimed to develop a reduced model of a transplanted tooth using the higher-order proper generalized decomposition method. The FE model of a previous study was used to learn von Mises root stress, and axial and lateral forces were used to simulate different occlusions between 75 and 175N. The error of the reduced model varied between 0.1% and 5.9% according to the subdomain, and was the highest for the highest lateral forces. The time for the FE simulation varied between 2.3 and 7.2 h. In comparison, the reduced model was built in 17s and interpolation of new results took approximately 2.10-2s. The use of MOR reduced the time for delivering the root stresses by a mean 5.9 h. The biomechanical behavior of a transplanted tooth simulated by FE models was accurately captured with a significant decrease of computing time. Future studies could include using jaw tracking devices for clinical use and the development of more realistic real-time simulations of tooth autotransplantation surgery.

Next generation antibacterial strategies for regenerative endodontic procedures: A scoping review.

BACKGROUND: The clinical results following regenerative endodontic procedures (REPs) vary according to numerous parameters, including the presence of bacteria. This limitation reduces the indications for REPs and calls for the development of next generation antibacterial strategies (NGAS) providing alternatives to current antibacterial strategies (CAS) such as double or triple antibiotic paste (DAP/TAP) and (Ca(OH)2). OBJECTIVES: The present scoping review aims to describe the current trends regarding the use of such strategies and highlight future perspectives. METHODS: Four databases (PUBMed, Cochrane, ClinicalTrials and Science Direct) were searched until 1st May 2023. RESULTS: A total of 918 records were identified, 133 were screened and assessed for eligibility, and 87 articles were included. The findings show that (1) clinical studies are only available for CAS, (2) although next generation strategies are the most studied approach since 2017, they are all at the pre-clinical stage, (3) most of the next generation strategies use galenic forms which offer cell support and colonization and which simultaneously contain antibacterial molecules as alternatives to CAS and to antibiotics in general, (4) standardization is required for future research, specifically regarding the bacterial strains studied, the use of biofilm studies and the cellular behaviour assessments. CONCLUSION: Although NGAS are promising strategies to improve REPs in the context of infection, the current evidence is mostly limited to pre-clinical studies. Further methodological improvement is required to allow relevant comparisons between studies and to reduce the time from bench to bedside.

Ethical considerations on artificial intelligence in dentistry: A framework and checklist.

OBJECTIVE: Artificial Intelligence (AI) refers to the ability of machines to perform cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance diagnostic accuracy, improve patient outcomes and streamline workflows. The present study provides a framework and a checklist to evaluate AI applications in dentistry from this perspective. METHODS: Lending from existing guidance documents, an initial draft of the checklist and an explanatory paper were derived and discussed among the groups members. RESULTS: The checklist was consented to in an anonymous voting process by 29 Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health's members. Overall, 11 principles were identified (diversity, transparency, wellness, privacy protection, solidarity, equity, prudence, law and governance, sustainable development, accountability, and responsibility, respect of autonomy, decision-making). CONCLUSIONS: Providers, patients, researchers, industry, and other stakeholders should consider these principles when developing, implementing, or receiving AI applications in dentistry. CLINICAL SIGNIFICANCE: While AI has become increasingly commonplace in dentistry, there are ethical concerns around its usage, and users (providers, patients, and other stakeholders), as well as the industry should consider these when developing, implementing, or receiving AI applications based on comprehensive framework to address the associated ethical challenges.

Assessment of guide fitting using an intra-oral scanner: An in vitro study.

OBJECTIVES: The aim of this study was to evaluate the capacity of an intra-oral scanner (IOS) to assess the position of an endodontic guide in vitro. METHODS: Fourteen extracted human teeth were placed into a maxillary model and scanned using computed tomography and a reference laboratory scanner. An ideal endodontic guide was then created and modified by adding defects of different thicknesses to simulate incorrect positions: 50 µm, 150 µm, 400 µm, and 1000 µm. For each thickness, guides were printed three times and each guide was scanned by three experimented operators using a Trios 4 IOS (3Shape, Copenhagen, Denmark). The 36 scans were compared using a best-fit alignment to the master model without defect to define the accuracy of the method and the positioning error. RESULTS: The IOS presented a mean trueness of 1.28 µm (SD= 12.70) and a mean precision of 11.52 µm (SD= 62.17). Considering all sizes of defect, the mean measured position of the endodontic guide was highly correlated (R>0.99) with the expected position. Compared to the ideal guide, there was a mean linear deviation of 46.11 µm (SD= 23.21) and a mean angular deviation of 5.9° (SD= 1.2); this deviation was not influenced by the operator. CONCLUSION: The present study found that an IOS had good performance to detect a positioning error of the endodontic guide in vitro. CLINICAL SIGNIFICANCE: This new application of IOS has a promising potential in clinical practice to assist practitioners during the fitting of guides.

Artificial Intelligence as a Decision-Making Tool in Forensic Dentistry: A Pilot Study with I3M.

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.

Prosthetic Management of Albers-Schönberg Disease with Osteomyelitis: A 10-Year Follow-up Case Report.

Albers-Schönberg disease is a rare bone syndrome characterized by increased bone density and infectious complications after dental extraction or minor surgery. The prosthodontic management of such edentulous patients with osteomyelitis is very challenging and requires special strategies due to a high risk of failure and worsening of the condition. This clinical report describes the rehabilitation of a 31-year-old edentulous woman presenting with Albers-Schönberg disease, secondary chronic osteomyelitis, maxillary hypoplasia, compromised oral conditions, temporomandibular disorders, and psychologic distress. The treatment included a mandibulectomy and removable prostheses. A crucial element for the successful long-term treatment and quality of life improvement observed in this patient was the 1-year transitional phase with interim dentures and frequent follow-up appointments. The complications and management proposed during a 10-year follow-up are presented. Int J Prosthodont 2023;36:642-648.

Artificial intelligence for sustainable oral healthcare.

OBJECTIVES: Oral health is grounded in the United National (UN) 2030 Agenda for Sustainable Developement and its 17 Goals (SDGs), in particular SDG 3 (Ensure healthy lives and promote well-being for all at all ages). The World Health Organization (WHO) Global Strategy on Oral Health calls for prioritizing environmentally sustainable and less invasive oral health care, and planetary health. Artificial Intelligence (AI) has the potential to power the next generation of oral health services and care, however its relationship with the broader UN and WHO concepts of sustainability remains poorly defined and articulated. We review the double-edged relationships between AI and oral health, to suggest actions that promote a sustainable deployment of AI for oral health. DATA: Concepts regarding AI, sustainability and sustainable development were identified and defined. A review of several double-edged relationship between AI and SDGs were exposed for the field of Oral Health. SOURCES: Medline and international declarations of the WHO, the UN and the World Dental Federation (FDI) were screened. STUDY SELECTION: One the one hand, AI may reduce transportation, optimize care delivery (SDG 3 "Good Health and Well-Being", SDG 13 "Climate Action"), and increase accessibility of services and reduce inequality (SDG 10 "Reduced Inequalities", SDG 4 "Quality Education"). On the other hand, the deployment, implementation and maintenance of AI require significant resources (SDG 12 "Responsible Consumption and Production"), and costs for AI may aggravate inequalities. Also, AI may be biased, reinforcing inequalities (SDG 10) and discrimination (SDG 5), and may violate principles of security, privacy and confidentiality of personal information (SDG 16). CONCLUSIONS: Systematic assessment of the positive impact and adverse effects of AI on sustainable oral health may help to foster the former and curb the latter based on evidence. CLINICAL SIGNIFICANCE: If sustainability imperatives are actively taken into consideration, the community of oral health professionals should then employ AI for improving effectiveness, efficiency, and safety of oral healthcare; strengthen oral health surveillance; foster education and accessibility of care; ensure fairness, transparency and governance of AI for oral health; develop legislation and infrastructure to expand the use of digital health technologies including AI.

Precision medicine using patient-specific modelling: state of the art and perspectives in dental practice.

The dental practice has largely evolved in the last 50 years following a better understanding of the biomechanical behaviour of teeth and its supporting structures, as well as developments in the fields of imaging and biomaterials. However, many patients still encounter treatment failures; this is related to the complex nature of evaluating the biomechanical aspects of each clinical situation due to the numerous patient-specific parameters, such as occlusion and root anatomy. In parallel, the advent of cone beam computed tomography enabled researchers in the field of odontology as well as clinicians to gather and model patient data with sufficient accuracy using image processing and finite element technologies. These developments gave rise to a new precision medicine concept that proposes to individually assess anatomical and biomechanical characteristics and adapt treatment options accordingly. While this approach is already applied in maxillofacial surgery, its implementation in dentistry is still restricted. However, recent advancements in artificial intelligence make it possible to automate several parts of the laborious modelling task, bringing such user-assisted decision-support tools closer to both clinicians and researchers. Therefore, the present narrative review aimed to present and discuss the current literature investigating patient-specific modelling in dentistry, its state-of-the-art applications, and research perspectives.

Multifactorial Analysis of Endodontic Microsurgery Using Finite Element Models.

BACKGROUND: The present study aimed to classify the relative contributions of four biomechanical factors-the root-end filling material, the apical preparation, the root resection length, and the bone height-on the root stresses of the resected premolar. METHODS: A design of experiments approach based on a defined subset of factor combinations was conducted to calculate the influence of each factor and their interactions. Sixteen finite element models were created and analyzed using the von Mises stress criterion. The robustness of the design of experiments was evaluated with nine supplementary models. RESULTS: The current study showed that the factors preparation and bone height had a high influence on root stresses. However, it also revealed that nearly half of the biomechanical impact was missed without considering interactions between factors, particularly between resection and preparation. CONCLUSIONS: Design of experiments appears to be a valuable strategy to classify the contributions of biomechanical factors related to endodontics. Imagining all possible interactions and their clinical impact is difficult and can require relying on one's own experience. This study proposed a statistical method to quantify the mechanical risk when planning apicoectomy. A perspective could be to integrate the equation defined herein in future software to support decision-making.

A critical analysis of research methods and experimental models to study pulpitis.

Pulpitis is the inflammatory response of the dental pulp to a tooth insult, whether it is microbial, chemical, or physical in origin. It is traditionally referred to as reversible or irreversible, a classification for therapeutic purposes that determines the capability of the pulp to heal. Recently, new knowledge about dental pulp physiopathology led to orientate therapeutics towards more frequent preservation of pulp vitality. However, full adoption of these vital pulp therapies by dental practitioners will be achieved only following better understanding of cell and tissue mechanisms involved in pulpitis. The current narrative review aimed to discuss the contribution of the most significant experimental models developed to study pulpitis. Traditionally, in vitro two (2D)- or three (3D)-dimensional cell cultures or in vivo animal models were used to analyse the pulp response to pulpitis inducers at cell, tissue or organ level. In vitro, 2D cell cultures were mainly used to decipher the specific roles of key actors of pulp inflammation such as bacterial by-products, pro-inflammatory cytokines, odontoblasts or pulp stem cells. However, these simple models did not reproduce the 3D organisation of the pulp tissue and, with rare exceptions, did not consider interactions between resident cell types. In vitro, tissue/organ-based models were developed to better reflect the complexity of the pulp structure. Their major disadvantage is that they did not allow the analysis of blood supply and innervation participation. On the contrary, in vivo models have allowed researchers to identify key immune, vascular and nervous actors of pulpitis and to understand their function and interplay in the inflamed pulp. However, inflammation was mainly induced by iatrogenic dentine drilling associated with simple pulp exposure to the oral environment or stimulation by individual bacterial by-products for short periods. Clearly, these models did not reflect the long and progressive development of dental caries. Lastly, the substantial diversity of the existing models makes experimental data extrapolation to the clinical situation complicated. Therefore, improvement in the design and standardisation of future models, for example by using novel molecular biomarkers, databased models and artificial intelligence, will be an essential step in building an incremental knowledge of pulpitis in the future.

Inferior alveolar nerve allogenic repair following mandibulectomy: A systematic review.

PURPOSE: Processed nerve allografts (PNA) are an alternative to nerve autografts to reconstruct the inferior alveolar nerve (IAN) when it is damaged. The purpose of this study was to report the results of IAN reconstruction using PNA in the context of aggressive benign mandibular pathology. MATERIAL AND METHOD: A systematic literature review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement through the MEDLINE (Pubmed) and SCOPUS (Elsevier) databases. Studies concerning reconstructive surgeries of IAN by PNA, performed at the same time as the surgical resection of the benign pathologies of the mandible were included. The following data were analyzed: gender and patient age, cause of mandibular resection, graft dimensions, sensory recovery at least 6 months after surgery according to the MRC scale, and adverse events related to the intervention. RESULTS: The initial search yielded 290 studies and 5 were included in the final review. A total of 33 patients underwent 36 IAN reconstructions; 14 patients were female (42.4%) and mean age was 30 years old. The mean length of graft used was 64.0 ± 9.1 mm. The most common pathology that led to nerve resection was ameloblastoma (52%). Among the reconstructions for which follow-up data were available, functional sensory recovery occurred in 92.9% of cases. CONCLUSION: PNA are a reliable, safe, and effective alternative to nerve autografts for the rehabilitation of the IAN with 92.9% of functional recovery according to the reported literature, avoiding any comorbidity associated with the use of a donor site.

Communication tools and patient satisfaction: A scoping review.

OBJECTIVE: This scoping review aimed to present an overview of the literature on communication tools in esthetic dentistry. A variety of communication tools have been proposed to include patients in the shared decision-making (SDM) workflow. Only little is known about implementing communication tools in dentistry and their impact on patient communication and patient satisfaction. A systematic literature search was performed in Medline, Embase, Cochrane, and World of Science to identify if communication tools have an impact on patient satisfaction. MATERIAL AND METHODS: The search included studies from January 1, 2000 to March 3, 2020 published in English, focusing on patient communication tools and patient satisfaction in esthetic dentistry. RESULTS: Out of 6678 records, 53 full-texts were examined. Ten studies were included. Data of the included studies were extracted systematically and subsequently analyzed. All studies found that patient communication utilizing specific communication tools positively impacted either patient satisfaction, patient-dentist relationship, information retention, treatment acceptance, quality of care or treatment outcome. CONCLUSIONS: Additional communication tools besides conventional verbal communication are able to enhance patient satisfaction, improve quality of care and establish a better patient-dentist relationship. It seems essential to further develop standardized communication tools for SDM in dental medicine, which will allow the comparison of research on this topic. CLINICAL SIGNIFICANCE: This scoping review shows the importance of patient involvement in the decision-making process for improved patient satisfaction with esthetic dental treatments. With an increased implementation of communication tools, patient satisfaction and SDM may further improve in the future.