ArticleJournal of pharmacy & bioallied sciences2025
Development of AI-Enhanced Smile Design Software for Ultra-Customized Aesthetic Outcomes.
Article in Journal of pharmacy & bioallied sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Perception of Orofacial Esthetics Using a Scan-Free Virtual Video Mock-Up: A Comparative Analysis of Self-Evaluation Versus Unknown-People Assessment.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2026Article
- AI-based shade matching versus visual assessment: Accuracy in dental aesthetics.Bioinformation · 2026Article
- Eco-Friendly Approaches to Enhance Dental Aesthetics and Patient Satisfactions Using Digital Smile Design: A Systematic Review.Clinical, cosmetic and investigational dentistry · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.