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Artificial Intelligence for Dentistry and Oral Sciences

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 May 2026 | Viewed by 494

Special Issue Editor

Maxillofacial Surgery Unit, Department of Head and Neck, San Camillo-Forlanini Hospital, Rome, Italy
Interests: head and neck cancer; maxillofacial surgery; reconstruction; oral surgery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Continuous evolution in basic sciences, technologies, and AI has influenced clinical practice across all medical specialties over the last two decades. This fact is represented in the key concept of applied sciences, which seek to discover new applications and perspectives in medicine and surgery. Eventually, special surgeries may benefit most from these contributions.

Applied sciences have many applications in maxillofacial surgery, from CAD/CAM in oncologic, reconstructive, and orthognathic surgeries to additively manufactured subperiosteal jaw implants (AMSJIs), bone regeneration grids, intraoperative navigation, and robotic surgery.

The aim of these advanced technologies is to effectively reduce the discrepancy between surgical planning and results, thereby improving patient outcomes while reducing biological costs. Additionally, surgical training is more reliable and effective when procedures are standardized using technologies and AI, which will eventually promote the development of new educational strategies for residents and fellows. Lastly, thanks to these technologies, surgeries can be initially performed on models and simulators, proactively identifying risks and challenges.

This Special Issue will synthesize papers on the use of applied sciences in maxillofacial surgery, assessing various application areas, ongoing studies, and perspectives.

Dr. Resi Pucci
Guest Editor

Manuscript Submission Information

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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. Applied Sciences 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 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

  • CAD/CAM
  • computer-aided design (CAD)/computer-aided manufacturing (CAM) technologies
  • virtual surgical planning (VSP)
  • additively manufactured subperiosteal jaw implant (AMSJI)
  • intraoperative navigation
  • robotic surgery
  • machine learning
  • high-definition 3D exoscope-assisted surgery

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Published Papers (1 paper)

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Research

14 pages, 584 KB  
Article
Artificial Intelligence Chatbots and Temporomandibular Disorders: A Comparative Content Analysis over One Year
by Serena Incerti Parenti, Alessandro Maglioni, Elia Evangelisti, Antonio Luigi Tiberio Gracco, Giovanni Badiali, Giulio Alessandri-Bonetti and Maria Lavinia Bartolucci
Appl. Sci. 2025, 15(23), 12441; https://doi.org/10.3390/app152312441 - 24 Nov 2025
Viewed by 296
Abstract
As the use of artificial intelligence (AI) chatbots for medical queries expands, their reliability may vary as models evolve. We longitudinally assessed the quality, reliability, and readability of information on temporomandibular disorders (TMD) generated by three widely used chatbots (ChatGPT, Gemini, and Microsoft [...] Read more.
As the use of artificial intelligence (AI) chatbots for medical queries expands, their reliability may vary as models evolve. We longitudinally assessed the quality, reliability, and readability of information on temporomandibular disorders (TMD) generated by three widely used chatbots (ChatGPT, Gemini, and Microsoft Copilot). Ten TMD questions were submitted to each chatbot at two timepoints (T1: February 2024; T2: February 2025). Two blinded evaluators independently assessed all answers using validated tools like the Global Quality Score (GQS), PEMAT, DISCERN, CLEAR, Flesch Reading Ease (FRE), and Flesch–Kincaid Grade Level (FKGL) tools. Analyses followed METRICS guidance. Comparisons between models and across timepoints were conducted using non-parametric tests. At T1, Copilot scored significantly lower in GQS, CLEAR appropriateness, and relevance (p < 0.01), while ChatGPT provided less evidence-based content than its counterparts (p < 0.001). Reliability was poor across models (mean DISCERN score: 34.73 ± 9.49), and readability was difficult (mean FRE: 34.64; FKGL: 14.13). At T2, performances improved across chatbots, particularly for Copilot, yet actionability remained limited and citations were inconsistent. This year-long longitudinal analysis shows an overall improvement in chatbot performance, although concerns regarding information reliability persist. These findings underscore the importance of human oversight of AI-mediated patient information, reaffirming that clinicians should remain the primary source of patient education. Full article
(This article belongs to the Special Issue Artificial Intelligence for Dentistry and Oral Sciences)
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