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<p>In orthodontics, the increasing use of AI-generated smile images in patient communication raises ethical and practical concerns about user perception and misinterpretation of these visuals. This cross-sectional, non-probabilistic sample study evaluated the ability of dentists, dental students, and laypeople to distinguish between real and AI-generated orthodontic smile images, and their perceived attractiveness. The final sample consisted of 288 participants, (63.4% female; mean age = 32.4 years)&#xa0;including 76 dentists, 63 dental students, and 149 laypeople. Each participant was presented with three clinical scenarios, mild dental misalignment, midline diastema, and moderate anterior crowding, and viewed randomized sets of images depicting pre-treatment, real post-treatment, and AI-generated smiles. For each image, participants indicated whether they believed it was AI-generated or real and rated its aesthetic appeal using a visual analog scale ranging from 0 to 100. Data were analyzed using descriptive statistics and diagnostic performance metrics (accuracy, sensitivity, specificity, PPV, and NPV), with attractiveness ratings compared between AI-generated and real images. Sensitivity for identifying AI-generated images was low across all groups (&lt; 50%), while specificity for recognizing real images was high (&gt; 87%). Dental students achieved the highest overall accuracy (72.7%), followed by laypeople (66.3%) and dentists (62.6%). AI-generated smiles were consistently rated as significantly more attractive than real outcomes by all groups (mean VAS: 78.8 vs. 37.9; <i>p</i> &lt; 0.001). AI-generated smile images were less accurately identified and more aesthetically pleasing than real clinical outcomes compared to real post-treatment outcomes, which were more consistently recognized across all participant groups, regardless of treatment type.</p>

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Perception of AI-generated smile versus real orthodontic treatment outcomes among dentists, students, and laypeople

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月21日

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  • Otso Tirkkonen,
  • Gil Guilherme Gasparello,
  • Sergio Luiz Mota-Júnior,
  • Claudia Trindade Mattos,
  • Marco Antonio Dias da Silva,
  • Matheus Melo Pithon,
  • Orlando Tanaka

摘要

In orthodontics, the increasing use of AI-generated smile images in patient communication raises ethical and practical concerns about user perception and misinterpretation of these visuals. This cross-sectional, non-probabilistic sample study evaluated the ability of dentists, dental students, and laypeople to distinguish between real and AI-generated orthodontic smile images, and their perceived attractiveness. The final sample consisted of 288 participants, (63.4% female; mean age = 32.4 years) including 76 dentists, 63 dental students, and 149 laypeople. Each participant was presented with three clinical scenarios, mild dental misalignment, midline diastema, and moderate anterior crowding, and viewed randomized sets of images depicting pre-treatment, real post-treatment, and AI-generated smiles. For each image, participants indicated whether they believed it was AI-generated or real and rated its aesthetic appeal using a visual analog scale ranging from 0 to 100. Data were analyzed using descriptive statistics and diagnostic performance metrics (accuracy, sensitivity, specificity, PPV, and NPV), with attractiveness ratings compared between AI-generated and real images. Sensitivity for identifying AI-generated images was low across all groups (< 50%), while specificity for recognizing real images was high (> 87%). Dental students achieved the highest overall accuracy (72.7%), followed by laypeople (66.3%) and dentists (62.6%). AI-generated smiles were consistently rated as significantly more attractive than real outcomes by all groups (mean VAS: 78.8 vs. 37.9; p < 0.001). AI-generated smile images were less accurately identified and more aesthetically pleasing than real clinical outcomes compared to real post-treatment outcomes, which were more consistently recognized across all participant groups, regardless of treatment type.