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Application of Artificial Intelligence in Dental and Craniofacial Research

A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Applied Dentistry and Oral Sciences".

Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 5565

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Department of Dentofacial Orthopedics and Orthodontics, Charité Universitätsmedizin Berlin, CC03, Aßmannshauser Straße 4-6, 14197 Berlin, Germany
Interests: digital orthodontics; individualized orthodontics; CAD/CAM based appliance design; bone–implant interactions; biologically driven orthodontic care
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Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has gained substantial public interest in recent years and is considered to have significant transformative potential in medicine and public health.

In dentistry and craniofacial surgery, several studies have already highlighted various fields of application, including automated image segmentation, the detection of landmarks and/or pathologies on radiographs, assistance in treatment planning, the prediction of treatment success or failure, and the prognosis of skeletal growth. 

This Special Issue aims to provide a comprehensive overview of the existing literature in the field of dentistry and craniofacial research, demonstrate the state-of-the-art application of AI in all disciplines, and present an outlook on potential future developments and applications.

We cordially welcome manuscripts on all aspects of AI applied to dentistry and oral health, focusing on orthodontics, periodontology, restorative dentistry, prosthodontics, implant dentistry, and oral and maxillofacial surgery.

Prof. Dr. Kathrin Becker
Guest Editor

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • anatomic landmark detection and/or analyses
  • automated treatment planning
  • prediction of treatment success, treatment failure, and/or treatment duration
  • prognosis of growth and development
  • evaluation of treatment outcomes
  • image segmentation, object detection, and object classification
  • detection of pathologies
  • big data
  • artificial neural networks
  • deep learning
  • machine learning
  • convolutional neural networks
  • fuzzy logic

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Published Papers (2 papers)

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Research

11 pages, 1093 KB  
Article
Repeatability of Artificial Intelligence Chatbots in Composite Shade Selection: Agreement with a Dental Specialist
by Seyit Bilal Ozdemir, Busra Ozdemir and Cagri Ural
Appl. Sci. 2026, 16(5), 2306; https://doi.org/10.3390/app16052306 - 27 Feb 2026
Cited by 1 | Viewed by 694
Abstract
This study aimed to evaluate the intra-model repeatability of three artificial intelligence-based chatbots (ChatGPT-4.0, Microsoft Copilot, and Claude 3.5) in composite shade selection and their agreement with a dental specialist. Ten acrylic resin maxillary central incisor teeth representing different VITA Classical shades ( [...] Read more.
This study aimed to evaluate the intra-model repeatability of three artificial intelligence-based chatbots (ChatGPT-4.0, Microsoft Copilot, and Claude 3.5) in composite shade selection and their agreement with a dental specialist. Ten acrylic resin maxillary central incisor teeth representing different VITA Classical shades (n = 10) were photographed together with A1, A2, and A3 composite shade tabs under standardized illumination. Shade selections were performed by each artificial intelligence model based on the photographs and repeated on five different days using identical images and prompts. Visual shade selection by the dental specialist was determined by consensus between two calibrated evaluators. CIE L*, a*, and b* values of the acrylic teeth and composite shade tabs were obtained by photometric analysis, and color differences were calculated using the CIEDE2000 formula. Intra-model repeatability was assessed using Fleiss’ kappa coefficient, and agreement with the dental specialist was evaluated using Cohen’s kappa statistic. Intra-model repeatability differed among the models, with ChatGPT-4.0 demonstrating fair repeatability (κ = 0.33), Claude 3.5 showing moderate repeatability (κ = 0.45), and Microsoft Copilot exhibiting poor repeatability (κ = −0.12). Trial-level agreement with the dental specialist varied across repeated assessments, with ChatGPT-4.0 generally demonstrating higher agreement than the other models, whereas Microsoft Copilot showed consistently low agreement. Artificial intelligence chatbots showed variable repeatability and limited agreement with expert evaluation in composite shade selection under standardized conditions. Full article
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25 pages, 2005 KB  
Article
Performance of Advanced Artificial Intelligence Models in Traumatic Dental Injuries in Primary Dentition: A Comparative Evaluation of ChatGPT-4 Omni, DeepSeek, Gemini Advanced, and Claude 3.7 in Terms of Accuracy, Completeness, Response Time, and Readability
by Berkant Sezer and Tuğba Aydoğdu
Appl. Sci. 2025, 15(14), 7778; https://doi.org/10.3390/app15147778 - 11 Jul 2025
Cited by 20 | Viewed by 4184
Abstract
This study aimed to evaluate and compare the performance of four advanced artificial intelligence-powered chatbots—ChatGPT-4 Omni (ChatGPT-4o), DeepSeek, Gemini Advanced, and Claude 3.7 Sonnet—in responding to questions related to traumatic dental injuries (TDIs) in the primary dentition. The assessment focused on accuracy, completeness, [...] Read more.
This study aimed to evaluate and compare the performance of four advanced artificial intelligence-powered chatbots—ChatGPT-4 Omni (ChatGPT-4o), DeepSeek, Gemini Advanced, and Claude 3.7 Sonnet—in responding to questions related to traumatic dental injuries (TDIs) in the primary dentition. The assessment focused on accuracy, completeness, readability, and response time, aligning with the 2020 International Association of Dental Traumatology guidelines. Twenty-five open-ended TDI questions were submitted to each model in two separate sessions. Responses were anonymized and evaluated by four pediatric dentists. Accuracy and completeness were rated using Likert scales; readability was assessed using five standard indices; and response times were recorded in seconds. ChatGPT-4o demonstrated significantly higher accuracy than Gemini Advanced (p = 0.005), while DeepSeek outperformed Gemini Advanced in completeness (p = 0.010). Response times differed significantly (p < 0.001), with DeepSeek being the slowest and ChatGPT-4o and Gemini Advanced being the fastest. DeepSeek produced the most readable outputs relatively, though none met public readability standards. Claude 3.7 generated the most complex texts (p < 0.001). A strong correlation existed between accuracy and completeness (ρ = 0.701, p < 0.001). These findings emphasize the cautious integration of artificial intelligence chatbots into pediatric dental care due to varied performance. Clinical accuracy, completeness, and readability are critical when offering information aligned with guidelines to support decisions in dental trauma management. Full article
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