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Application of Artificial Intelligence in Dental and Craniofacial Research

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Dentistry and Oral Sciences".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 737

Special Issue Editor


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Guest Editor
Department for Orthodontics and Dentofacial Orthopedics, Charité - Universitätsmedizin Berlin, 14197 Berlin, Germany
Interests: micro-CT; orthodontic mini-implants; osseointegration; medical imaging; bone regeneration; orthodontics
Special Issues, Collections and Topics in MDPI journals

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 (1 paper)

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Research

25 pages, 2005 KiB  
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
Viewed by 421
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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