ArticleBDJ open2025
Comparative analysis of facial aesthetics in AI generated versus conventionally crafted digital smile designs-a cross-sectional study.
Article in BDJ open, 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.
- Comparing Facial Attractiveness and Professional Acceptance Between Real and AI-Generated Orthodontic Treatment Outcome Images.Orthodontics & craniofacial research · 2026Article
- Present and Future of the Use of Artificial Intelligence in Orthodontics.Bioengineering (Basel, Switzerland) · 2026Review
- AI-based shade matching versus visual assessment: Accuracy in dental aesthetics.Bioinformation · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThis study aimed to evaluate the aesthetic preferences of traditional digital smile designs and artificial intelligence (AI)-generated smile designs among dentists, dental students, and laypersons, addressing gaps in previous research on the clinical acceptability of AI in prosthodontic aesthetics. MATERIALS AND
methodsA cross-sectional, questionnaire-based study was conducted via an online survey distributed across India between 2024 and 2025. A total of 320 participants, including dental students, dentists, and nondental professionals, were recruited on the basis of calculated sample size requirements. Smile designs were created for four clinical cases via Exo-CAD software, employing two methods: conventional manual design by prosthodontists and AI-based automated design. The participants evaluated paired smile designs and indicated their aesthetic preferences. Demographic data were also collected. Chi-square (χ²) tests were applied for statistical analysis, with a significance level set at p < 0.05.
resultsNo significant differences in aesthetic preferences were observed based on sex, age, or occupation. Overall, manually crafted smile designs were consistently preferred across all the participant categories. However, AI-generated smiles for Cases 3 and 4 presented relatively higher acceptance rates (39.4% and 39.7%, respectively) than those for Cases 1 and 2 did. The findings suggest that while AI algorithms can achieve acceptable levels of aesthetic appeal, they still lack the human touch essential for capturing nuanced facial dynamics and emotional context.
conclusionAlthough AI-based smile design systems demonstrate promise in improving workflow efficiency and consistency, they are currently unable to replicate the individualized artistic judgment of experienced clinicians. Manual intervention remains critical for achieving truly personalized and aesthetically harmonious outcomes. Future approaches should consider hybrid models that combine AI automation with clinician-led customization to increase both the efficiency and patient satisfaction of smile aesthetics.
Identifiers
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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.