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Diagnostics
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Application of Artificial Intelligence to Oral Diseases
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Application of Artificial Intelligence to Oral Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 976

Special Issue Editor

Prof. Dr. Gang Li

E-Mail Website
Guest Editor
Peking University School and Hospital of Stomatology, Beijing, China
Interests: digital radiology; AI-driven diagnosis in oral health; radiation dosimetry and radiation protection

Special Issue Information

Dear Colleagues,

AI technologies exhibit remarkable vitality in the diagnosis, treatment planning, and outcome prediction of oral diseases. This Special Issue, dedicated to the "Application of Artificial Intelligence to Oral Diseases", aims to highlight the latest advancements, challenges, and future directions in leveraging AI to enhance the diagnosis, treatment, and management of oral diseases. As AI technologies, including machine learning, deep learning, computer vision, and natural language processing, continue to evolve, their integration into dentistry offers unprecedented opportunities for improving patient care, streamlining workflows, and advancing precision medicine in oral health.

Scope and Topics of Interest

This Special Issue seeks the submission of high-quality original research articles, systematic reviews, case studies, and perspective papers that explore the intersection of AI and oral healthcare. Topics of interest include, but are not limited to, the following:

  • AI in Diagnostic Imaging: Automated analysis of dental radiographs, cone beam computed tomography (CBCT), intraoral scans, and optical imaging for detecting caries, periodontal disease, periapical lesions, and oral cancers.
  • Early Detection and Risk Prediction: Machine learning models for predicting disease progression (e.g., periodontitis, oral squamous cell carcinoma) and personalized risk assessment.
  • Treatment Planning and Robotics: AI-assisted decision support systems for implantology, orthodontics, and maxillofacial surgery, including robotic-assisted interventions.
  • Natural Language Processing (NLP): Applications in electronic health records (EHRs), clinical note analysis, and automated patient communication.
  • Teledentistry and AI: Remote monitoring, AI-powered chatbots for patient triage, and mobile-based diagnostic tools.
  • Ethical, Legal, and Regulatory Considerations: Challenges in data privacy, algorithmic bias, and the clinical validation of AI models in dentistry.

This Special Issue provides a platform for interdisciplinary collaboration among dentists, computer scientists, engineers, and policymakers to discuss innovations that bridge the gap between AI research and clinical practice. Accepted papers will contribute to a growing body of knowledge that could redefine the standards of care in oral medicine.

Prof. Dr. Gang Li
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • oral disease
  • artificial intelligence
  • AI-driven diagnosis
  • oral radiology
  • cone beam computed tomography (CBCT)

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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23 pages, 4371 KiB  
Article
Advances in Periodontal Diagnostics: Application of MultiModal Language Models in Visual Interpretation of Panoramic Radiographs
by Albert Camlet, Aida Kusiak, Agata Ossowska and Dariusz Świetlik
Diagnostics 2025, 15(15), 1851; https://doi.org/10.3390/diagnostics15151851 - 23 Jul 2025
Viewed by 167
Abstract
Background: Periodontitis is a multifactorial disease leading to the loss of clinical attachment and alveolar bone. The diagnosis of periodontitis involves a clinical examination and radiographic evaluation, including panoramic images. Panoramic radiographs are cost-effective methods widely used in periodontitis classification. The remaining [...] Read more.
Background: Periodontitis is a multifactorial disease leading to the loss of clinical attachment and alveolar bone. The diagnosis of periodontitis involves a clinical examination and radiographic evaluation, including panoramic images. Panoramic radiographs are cost-effective methods widely used in periodontitis classification. The remaining bone height (RBH) is a parameter used to assess the alveolar bone level. Large language models are widely utilized in the medical sciences. ChatGPT, the leading conversational model, has recently been extended to process visual data. The aim of this study was to assess the effectiveness of the ChatGPT models 4.5, o1, o3 and o4-mini-high in RBH measurement and tooth counts in relation to dental professionals’ evaluations. Methods: The analysis was based on 10 panoramic images, from which 252, 251, 246 and 271 approximal sites were qualified for the RBH measurement (using the models 4.5, o1, o3 and o4-mini-high, respectively). Three examiners were asked to independently evaluate the RBH in approximal sites, while the tooth count was achieved by consensus. Subsequently, the results were compared with the ChatGPT outputs. Results: ChatGPT 4.5, ChatGPT o3 and ChatGPT o4-mini-high achieved substantial agreement with clinicians in the assessment of tooth counts (κ = 0.65, κ = 0.66, κ = 0.69, respectively), while ChatGPT o1 achieved moderate agreement (κ = 0.52). In the context of RBH values, the ChatGPT models consistently exhibited a positive mean bias compared with the clinicians. ChatGPT 4.5 was reported to provide the lowest bias (+12 percentage points (pp) for the distal surfaces, width of the 95% CI for limits of agreement (LoAs) ~60 pp; +11 pp for the mesial surfaces, LoA width ~54 pp). Conclusions: ChatGPT 4.5 and ChatGPT o3 show potential in the assessment of tooth counts on a panoramic radiograph; however, their present level of accuracy is insufficient for clinical use. In the current stage of development, the ChatGPT models substantially overestimated the RBH values; therefore, they are not applicable for classifying periodontal disease. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence to Oral Diseases)
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42 pages, 5287 KiB  
Article
Enhancing Early Detection of Oral Squamous Cell Carcinoma: A Deep Learning Approach with LRT-Enhanced EfficientNet-B3 for Accurate and Efficient Histopathological Diagnosis
by A. A. Abd El-Aziz, Mahmood A. Mahmood and Sameh Abd El-Ghany
Diagnostics 2025, 15(13), 1678; https://doi.org/10.3390/diagnostics15131678 - 1 Jul 2025
Viewed by 634
Abstract
Background/Objectives: Oral cancer, particularly oral squamous cell carcinoma (OSCC), ranks as the sixth most prevalent cancer globally, with rates of occurrence on the rise. The diagnosis of OSCC primarily depends on histopathological images (HIs), but this method can be time-intensive, expensive, and reliant [...] Read more.
Background/Objectives: Oral cancer, particularly oral squamous cell carcinoma (OSCC), ranks as the sixth most prevalent cancer globally, with rates of occurrence on the rise. The diagnosis of OSCC primarily depends on histopathological images (HIs), but this method can be time-intensive, expensive, and reliant on specialized expertise. Manual diagnosis often leads to inaccuracies and inconsistencies, highlighting the urgent need for automated and dependable diagnostic solutions to enhance early detection and treatment success. Methods: This research introduces a deep learning (DL) approach utilizing EfficientNet-B3, complemented by learning rate tuning (LRT), to identify OSCC from histopathological images. The model is designed to automatically modify the learning rate based on the accuracy and loss during training, which improves its overall performance. Results: When evaluated using the oral tumor dataset from the multi-cancer dataset, the model demonstrated impressive results, achieving an accuracy of 99.84% and a specificity of 99.92%, along with other strong performance metrics. Conclusions: These findings indicate its potential to simplify the diagnostic process, lower costs, and enhance patient outcomes in clinical settings. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence to Oral Diseases)
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