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Oral
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Artificial Intelligence in Oral Medicine: Advancements and Challenges
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Artificial Intelligence in Oral Medicine: Advancements and Challenges

A special issue of Oral (ISSN 2673-6373).

Deadline for manuscript submissions: 15 July 2025 | Viewed by 5804

Special Issue Editor

Dr. Xiaoyuan Han

E-Mail Website
Guest Editor
Department of Biomedical Sciences, Arthur A. Dugoni School of Dentistry, University of the Pacific, San Francisco, CA 94103, USA
Interests: pharmacology; immunology; oral cancer; cardiovascular diseases; metabolic disorder; nutrition

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), particularly machine learning and deep learning, is rapidly transforming oral medicine. These technologies offer immense potential for improving diagnostic accuracy and efficiency in clinical image analysis and documentation. However, significant challenges remain. This Special Issue of Oral seeks to explore both the advancements and challenges of AI in oral medicine. We invite submissions that address topics including, but not limited to, the following:

  • Developing robust and generalizable AI models for diverse dental clinical settings;
  • Overcoming legal, ethical, and financial hurdles to AI implementation;
  • Integrating AI into existing dental workflows and practices;
  • Evaluating the impact of AI on patient outcomes and experiences;
  • Addressing bias and fairness in AI algorithms for oral healthcare;
  • Exploring the future of AI in oral medicine education.

We encourage submissions from researchers, clinicians, educators, and other stakeholders engaged in the development and application of AI in oral medicine. By fostering a deeper understanding of the potential as well as the limitations of this transformative technology, we aim to accelerate its responsible adoption and contribute to improved oral healthcare for all.

Dr. Xiaoyuan Han
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. Oral is an international peer-reviewed open access quarterly 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 1000 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

  • AI models for diverse dental clinical settings
  • AI algorithms for oral healthcare
  • AI in oral medicine education

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Published Papers (4 papers)

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Research

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14 pages, 593 KiB  
Article
Predicting Artificial Intelligence Acceptance in Dental Treatments Among Patients in Saudi Arabia: A Perceived Risks and Benefits Perspective
by Rayan Sharka, Bayan Skatawi, Ghaday Sayyam, Maya Abutaleb, Mawadah Alshareef, Mohammed Alamar, Lujain Abualkhair and Yousef Ezzat
Oral 2025, 5(2), 28; https://doi.org/10.3390/oral5020028 - 16 Apr 2025
Viewed by 303
Abstract
Background/Objectives: Artificial Intelligence (AI) is transforming dentistry by offering advanced solutions to improve diagnostic accuracy, optimize treatment planning, and advance patient care. However, as AI becomes more prevalent in dental practice, patients may have concerns and skepticism about its implementation. Therefore, this study [...] Read more.
Background/Objectives: Artificial Intelligence (AI) is transforming dentistry by offering advanced solutions to improve diagnostic accuracy, optimize treatment planning, and advance patient care. However, as AI becomes more prevalent in dental practice, patients may have concerns and skepticism about its implementation. Therefore, this study aims to explore the impact of the perceived risks and benefits on patients’ willingness to accept AI in dental treatment. Methods: This cross-sectional study was conducted in two public dental hospitals, and 586 patients were invited to complete a 28-item questionnaire. In total, 511 questionnaires were completed, resulting in a response rate of 87%. Multiple regression analysis was performed to assess the impact of perceived risks and benefits on patients’ willingness to accept AI in dental treatment. Results: All dimensions of perceived benefits had higher mean scores compared to the perceived risks. Additionally, three perceived benefit dimensions had a significant positive influence on the willingness to accept AI: patient-enhanced experience (β = 47.1, p < 0.001), personalized dental care (β = 22.2, p < 0.001), and cost efficiency (β = 15.3, p < 0.001). Conclusions: The perceived risks had little impact on patients’ willingness to accept AI, suggesting patients may be unaware of or unconcerned about AI’s potential risks in dentistry. Future research should investigate these perceptions and other dimensions influencing AI acceptance. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Medicine: Advancements and Challenges)
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26 pages, 5178 KiB  
Article
Estimating Age and Sex from Dental Panoramic Radiographs Using Neural Networks and Vision–Language Models
by Salem Shamsul Alam, Nabila Rashid, Tasfia Azrin Faiza, Saif Ahmed, Rifat Ahmed Hassan, James Dudley and Taseef Hasan Farook
Oral 2025, 5(1), 3; https://doi.org/10.3390/oral5010003 - 8 Jan 2025
Cited by 1 | Viewed by 1925
Abstract
Purpose: The purpose of this study was to compare multiple deep learning models for estimating age and sex using dental panoramic radiographs and identify the most successful deep learning models for the specified tasks. Methods: The dataset of 437 panoramic radiographs was divided [...] Read more.
Purpose: The purpose of this study was to compare multiple deep learning models for estimating age and sex using dental panoramic radiographs and identify the most successful deep learning models for the specified tasks. Methods: The dataset of 437 panoramic radiographs was divided into training, validation, and testing sets. Random oversampling was used to balance the class distributions in the training data and address the class imbalance in sex and age. The models studied were neural network models (CNN, VGG16, VGG19, ResNet50, ResNet101, ResNet152, MobileNet, DenseNet121, DenseNet169) and vision–language models (Vision Transformer and Moondream2). Binary classification models were built for sex classification, while regression models were developed for age estimations. Sex classification was evaluated using precision, recall, F1 score, accuracy, area under the curve (AUC), and a confusion matrix. For age regression, performance was evaluated using mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R2, and mean absolute percentage error (MAPE). Results: In sex classification, neural networks achieved accuracies of 85% and an AUC of 0.85, while Moondream2 had much lower accuracy (49%) and AUC (0.48). DenseNet169 performed better than other models for age regression, with an R2 of 0.57 and an MAE of 7.07. Among sex classes, the CNN model achieved the highest precision, recall, and F1 score for both males and females. Vision Transformers that specialised in identifying objects from images demonstrated weaker performance in dental panoramic radiographs, with an inference time of 4.5 s per image. Conclusions: The CNN and DenseNet169 were the most effective models for classifying sex and age regression, performing better than other models for estimating age and sex from dental panoramic radiographs. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Medicine: Advancements and Challenges)
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Review

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15 pages, 649 KiB  
Review
The Integration of Salivary pH Meters and Artificial Intelligence in the Early Diagnosis and Management of Dental Caries in Pediatric Dentistry: A Scoping Review
by Eliza Denisa Sgiea, Corina Marilena Cristache, Tamara Mihut, Sergiu Drafta and Irina Adriana Beuran
Oral 2025, 5(1), 12; https://doi.org/10.3390/oral5010012 - 10 Feb 2025
Viewed by 1413
Abstract
Dental caries is one of the most prevalent chronic conditions among children globally. Salivary pH monitoring, an essential diagnostic parameter, plays a critical role in understanding caries risk and oral health. This scoping review aims to evaluate the application of digital salivary pH [...] Read more.
Dental caries is one of the most prevalent chronic conditions among children globally. Salivary pH monitoring, an essential diagnostic parameter, plays a critical role in understanding caries risk and oral health. This scoping review aims to evaluate the application of digital salivary pH meters in pediatric dentistry, particularly in caries diagnosis and prevention, while exploring the potential integration of artificial intelligence (AI) in this domain. Methods: A literature search was conducted across the PubMed, Web of Science, and Scopus databases for studies published between 2014 and 2024. The inclusion criteria focused on clinical studies involving children aged 1 to 18 years and the use of digital salivary pH meters. Studies that utilized AI in conjunction with salivary pH monitoring were also reviewed. Data were extracted and analyzed to assess the effectiveness of pH meters in caries detection and their role in broader oral health applications. Results: Out of 549 articles screened, 11 met the inclusion criteria. The review highlighted the utility of digital pH meters for assessing caries risk, monitoring dietary impacts, and evaluating the effectiveness of preventive treatments. However, none of the studies combined salivary pH monitoring with AI. Emerging technologies, such as smartphone-based pH sensors, have demonstrated promising applications for real-time, non-invasive diagnostics. Conclusions: Digital salivary pH meters provide precise and reproducible measurements, significantly enhancing caries risk assessment and preventive strategies in pediatric dentistry. While AI integration remains unexplored in this context, its potential to refine risk prediction models and personalize treatments underscores the need for future research in this area. These advancements could improve caries prevention and management, enhancing pediatric oral health outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Medicine: Advancements and Challenges)
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9 pages, 199 KiB  
Review
Current AI Applications and Challenges in Oral Pathology
by Zaizhen Xu, Alice Lin and Xiaoyuan Han
Oral 2025, 5(1), 2; https://doi.org/10.3390/oral5010002 - 6 Jan 2025
Viewed by 1535
Abstract
Artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) techniques such as convolutional neural networks (CNNs) and natural language processing (NLP), has shown remarkable promise in image analysis and clinical documentation in oral pathology. In order to explore the transformative [...] Read more.
Artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) techniques such as convolutional neural networks (CNNs) and natural language processing (NLP), has shown remarkable promise in image analysis and clinical documentation in oral pathology. In order to explore the transformative potential of artificial intelligence (AI) in oral pathology, this review highlights key studies demonstrating current AI’s improvement in oral pathology, such as detecting oral diseases accurately and streamlining diagnostic processes. However, several limitations, such as data quality, generalizability, legal and ethical considerations, financial constraints, and the need for paradigm shifts in practice, are critically examined. Addressing these challenges through collaborative efforts, robust validation, and strategic integration can pave the way for AI to revolutionize oral pathology, ultimately improving patient outcomes and advancing the field. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Medicine: Advancements and Challenges)
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