Comparative analysis of facial aesthetics in AI generated versus conventionally crafted digital smile designs—a cross-sectional study

This study evaluates the preference rates for smile designs created by professionals or by Artificial Intelligence (AI) among dentists, dentistry students, and laypeople. Four cases with symmetrical and asymmetrical features were selected based on the Facial Flow (FF) concept from the database of the Smile Designer app regarding anatomical facial points. Two smile designs were created for each selected case: one using Artificial Intelligence (AI) and one created manually. An online survey assessed participants’ preferences for the different smile designs. The chi-square test “Pearson’s and Fisher’s exact test (P)” was used to analyze the survey data. A total of 628 people completed the study. Dentists preferred the manually-created smile design for the first three cases. For Case 4, dentists who used the Smile Designer program preferred the manually-created design (55.88%), while those who did not use the program preferred the AI-generated design (55.84%). There was a significant difference in esthetic perception between dentists and dental students (p = 0.001) and between dentists and laypeople (p = 0.001) for Case 1, only between dentists and dental students (p = 0.003) for Case 2, and only between dentists and laypeople (p = 0.001) for Case 3. Furthermore, we found that females (p = 0.007) and orthodontists (p = 0.025) had a higher preference for the AI-generated design in this case compared to males and other dental specialties for Case 3. While age, education level, and clinical experience did not significantly impact dentists’ preference for manually-created or AI-generated smile designs (p > 0.05), our results suggest that there were some differences in preference for Case 3. Overall, our findings suggest that the use of AI-generated smile designs for symmetric faces is acceptable to both dentists and laypeople and can offer time-saving benefits for clinicians.

Artificial intelligence: Friend or foe?

Artificial intelligence (AI) applications are growing in smile design and aesthetic procedures. The current expansion and performance of AI models in digital smile design applications have not yet been systematically documented and analyzed. The purpose of this review was to assess the performance of AI models in smile design, assess the criteria of points of reference using AI analysis, and assess different AI software performance. An electronic review was completed in five databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. Studies that developed AI models for smile design were included. The search strategy included articles published until November 1, 2024. Two investigators independently evaluated the quality of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies and Textual Evidence: Expert Opinion Results. The search resulted in 2653 articles. A total of 2649 were excluded according to the exclusion criteria after reading the title, abstract, and/or full-text review. Four articles published between 2023 and 2024 were included in the present investigation. Two articles compared 2D and 3D points while one article compared the outcome of satisfaction between dentists and patients, and the last article emphasized the ethical components of using AI. The results of the studies reviewed in this paper suggest that AI-generated smile designs are not significantly different from manually created designs in terms of esthetic perception. 3D designs are more accurate than 2D designs and offer more advantages. More articles are needed in the field of AI and smile design.

Assessment of information quality in contemporary artificial intelligence systems for digital smile design: A comparative analysis.

Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines.

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. 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. 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. 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.

The esthetics of a smile holds significant importance in an individual's self-esteem and overall quality of life. In the realm of cosmetic dentistry, smile design has traditionally relied on conventional methods, but recent advances in technology have introduced digital smile design (DSD) as a promising alternative. In this randomized controlled trial, 150 adult patients seeking smile enhancement procedures were enrolled and randomly assigned to one of two groups: the DSD group or the conventional smile design group. The DSD group underwent smile design using digital technology, including intraoral scans, computer-aided design, and 3D simulations. Meanwhile, the conventional smile design group received smile design through traditional methods, involving manual impressions, stone models, and manual wax-ups. Patient satisfaction was measured using a visual analog scale (VAS) ranging from 0 to 100 immediately after the procedure, while treatment outcomes were assessed three months post-procedure by dental professionals using a standardized assessment scale. In terms of patient satisfaction, the DSD group demonstrated a mean score of 85.4 (SD ± 6.2), while the conventional smile design group had a mean score of 79.8 (SD ± 7.1). This suggests that patients in the DSD group reported higher levels of satisfaction with their smile enhancements. Regarding treatment outcomes, 92% of patients in the DSD group exhibited excellent restoration fit, occlusion, and esthetics, whereas 78% of patients in the conventional smile design group achieved the same level of excellence. These findings collectively indicate that digital smile design (DSD) may yield superior patient satisfaction and improved treatment outcomes when compared to conventional smile design methods, particularly with regard to esthetic results and overall patient contentment. In conclusion, the results of this randomized controlled trial emphasize the potential advantages of integrating digital technology into smile design procedures.

Artificial Intelligence for Early Prediction of Pulmonary Hypertension Using Electrocardiography

Trusted evidence. Informed decisions. Better health.

The application of parameters for comprehensive smile esthetics by digital smile design programs: A review of literature

ABSTRACTBackground:The integration of artificial intelligence (AI) in dentistry, particularly in smile design and prosthetic planning, represents a significant advancement in digital dentistry. Despite growing technological capabilities, there remains a knowledge gap in clinical awareness and adoption.Aim:To assess the awareness, current utilization, and perceptions of AI-based tools for smile design and prosthetic planning among dental professionals.Materials and Methods:A cross-sectional online survey was conducted using a structured questionnaire. The survey was distributed among general dentists, prosthodontists, periodontists, oral surgeons, and postgraduate students across India. Responses were analyzed to evaluate awareness levels, usage trends, perceived advantages, and barriers to adoption.Results:Out of 320 respondents, 68% were aware of AI applications in smile design, while only 23% had implemented AI tools in clinical practice. The most commonly cited advantages were improved accuracy (76%) and enhanced patient communication (64%). Barriers included cost, lack of training, and limited access to AI-integrated systems.Conclusion:Although awareness of AI in smile design and prosthetic planning is increasing, clinical adoption remains low. Continued professional education, cost reduction, and academic curriculum integration may accelerate the adoption of AI technologies in routine prosthodontic and esthetic practice.

ABSTRACTBackground:Advancements in artificial intelligence (AI) have paved the way for ultra-customized aesthetic solutions in dentistry, particularly in smile design. Conventional smile design methods often fall short in providing a fully personalized outcome, necessitating the development of AI-enhanced software to optimize results by considering facial features, dental parameters, and patient preferences.Materials and Methods:A prototype AI-enhanced smile design software was developed using a combination of convolutional neural networks for facial analysis and generative adversarial networks for creating customized smile designs. The study involved 50 participants, each undergoing facial feature scanning, digital dental impressions, and patient-specific aesthetic input collection. The software’s performance was evaluated based on user satisfaction, aesthetic quality, and procedural efficiency. A comparison was made with conventional smile design methods to assess improvements in outcomes.Results:The AI-enhanced software demonstrated significant improvements in aesthetic outcomes and efficiency. The mean patient satisfaction score was 9.2/10 compared to 7.5/10 with conventional methods. Aesthetic quality was rated higher by experts (mean score: 8.8/10 vs. 7.3/10), and the time required for smile design reduced by 40%. The integration of AI allowed for more precise customization, aligning with patient preferences and anatomical considerations.Conclusion:The development of AI-enhanced smile design software represents a significant step toward achieving ultra-customized aesthetic outcomes in dentistry. By integrating advanced facial analysis and design algorithms, the software offers a superior alternative to conventional methods, promising enhanced satisfaction, efficiency, and aesthetic precision.

The use of digital breast tomosynthesis (DBT) in breast cancer screening has become widely accepted, facilitating increased cancer detection and lower recall rates compared with those achieved by using full-field digital mammography (DM). However, the use of DBT, as compared with DM, raises new challenges, including a larger number of acquired images and thus longer interpretation times. While most current artificial intelligence (AI) applications are developed for DM, there are multiple potential opportunities for AI to augment the benefits of DBT. During the diagnostic steps of lesion detection, characterization, and classification, AI algorithms may not only assist in the detection of indeterminate or suspicious findings but also aid in predicting the likelihood of malignancy for a particular lesion. During image acquisition and processing, AI algorithms may help reduce radiation dose and improve lesion conspicuity on synthetic two-dimensional DM images. The use of AI algorithms may also improve workflow efficiency and decrease the radiologist's interpretation time. There has been significant growth in research that applies AI to DBT, with several algorithms approved by the U.S. Food and Drug Administration for clinical implementation. Further development of AI models for DBT has the potential to lead to improved practice efficiency and ultimately improved patient health outcomes of breast cancer screening and diagnostic evaluation. See the invited commentary by Bahl in this issue. ©RSNA, 2022.

Artificial Intelligence (AI) has revolutionized the healthcare sector by improving patient care and treatment through diagnostic revolutionization. AI is used for diagnosing and detecting diseases, analyzing large-scale patient data sets to find trends and abnormalities. This has led to increased precision and speed of disease identification, enabling early intervention and individualized treatment programs. AI-driven diagnostic systems have shown effectiveness in reducing incorrect diagnoses and enhancing patient outcomes for diseases like diabetes, cancer, and heart issues.AI algorithms also aid in treatment planning and drug discovery, predicting patient responses to treatments and optimizing therapeutic strategies. In clinical settings, AI-powered systems automate administrative tasks, manage patient records, and improve workflow efficiency. Chatbots and virtual health assistants can offer patient guidance and support, reducing healthcare staff burden and enhancing patient experiences. However, AI integration in healthcare faces challenges such as data privacy, security, financial resources, and ethical considerations. Bias in AI algorithms can perpetuate healthcare disparities, and efforts are being made to reduce bias through diverse datasets and transparent AI systems. Legal and ethical frameworks are needed to address these issues.In conclusion, AI in healthcare has the potential to improve patient outcomes, but challenges such as funding, security, data privacy, and ethical considerations need to be addressed.

Integrating digital smile design into restorative Dentistry: A narrative review of the applications and benefits