Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives, 2nd Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 477

Special Issue Editors


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Guest Editor
Institute for Translational Research in Dentistry, Kyungpook National University, 2175 Dalgubeoldaero, Jung-Gu, Daegu 41940, Republic of Korea
Interests: aesthetic dentistry; applied artificial intelligence; applied 3D image technology; biomaterials
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Guest Editor
Department of Reconstructive Dentistry, University Center for Dental Medicine Basel (UZB), University of Basel, CH-4058 Basel, Switzerland
Interests: reconstructive dentistry; prosthodontics; implant dentistry; digital technology; dental materials; augmented/virtual reality; artificial intelligence; big data & eHealth; public health; translational research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will explore the transformative role of artificial intelligence (AI) in the field of dentistry, shedding light on innovative applications, emerging technologies, and their potential impact on patient care. As the intersection of AI and dentistry continues to evolve, this Special Issue will feature cutting-edge research, reviews, and case studies that showcase the integration of AI algorithms, machine learning, and data analytics in various aspects of dental practice.

Revolutionizing traditional paradigms, this Special Issue will showcase how AI is reshaping diagnostic imaging, treatment planning, and patient management. Furthermore, it will unveil the transformative potential of machine learning and data analytics in streamlining dental practices, enhancing precision and optimizing treatment outcomes. From leveraging advanced algorithms for accurate diagnostics to incorporating state-of-the-art technologies for treatment personalization, the contributions published in this Special Issue will illuminate the multifaceted impact of AI on the entire spectrum of oral healthcare.

This Special Issue will not only explore technological advancements, but also it is an invitation to envision a future where AI is not merely a tool but a transformative force in the pursuit of optimal oral health. Additionally, it will address challenges, ethical considerations, and opportunities for collaboration between dental professionals and AI experts.

Prof. Dr. Hang Nga Mai
Prof. Dr. Tim Joda
Guest Editors

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Keywords

  • artificial intelligence
  • big data in dentistry
  • explainable AI in dental diagnostics
  • blockchain in dental data security
  • natural language processing in dental records
  • robotics in dentistry
  • smart dental devices
  • AI in clinical decision support
  • automation in dentistry
  • AI-based diagnostic imaging
  • AI-supported treatment planning

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Published Papers (1 paper)

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Research

13 pages, 484 KB  
Article
Mathematical and AI-Based Predictive Modelling for Dental Caries Risk Using Clinical and Behavioural Parameters
by Liliana Sachelarie, Ioana Scrobota, Roxana Alexandra Cristea, Ramona Hodișan, Mihail Pantor and Gabriela Ciavoi
Bioengineering 2025, 12(11), 1190; https://doi.org/10.3390/bioengineering12111190 - 31 Oct 2025
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Abstract
Dental caries remains one of the most prevalent chronic diseases worldwide, driven by complex interactions among dietary, hygienic, and biological factors. This study introduces a hybrid predictive framework that integrates mathematical modelling and artificial intelligence (AI) to estimate individual caries risk based on [...] Read more.
Dental caries remains one of the most prevalent chronic diseases worldwide, driven by complex interactions among dietary, hygienic, and biological factors. This study introduces a hybrid predictive framework that integrates mathematical modelling and artificial intelligence (AI) to estimate individual caries risk based on daily sugar intake, oral hygiene index, salivary pH, fluoride exposure, age, and sex. A first-order balance differential equation was applied to simulate demineralisation–remineralisation dynamics, while a feed-forward artificial neural network (ANN) was trained on simulated and literature-derived datasets. The hybrid model demonstrated strong predictive performance, achieving 91.2% accuracy and an AUC of 0.98 in classifying individuals into low-, moderate-, and high-risk categories. Sensitivity analysis identified sugar intake and oral hygiene as dominant determinants, while fluoride and salivary pH showed protective effects. These findings highlight the feasibility of combining mechanistic and data-driven approaches to enhance early risk assessment and support the development of intelligent, personalised screening tools in preventive dentistry. Full article
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