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Dentistry Journal
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Artificial Intelligence in Oral Rehabilitation
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Artificial Intelligence in Oral Rehabilitation

A special issue of Dentistry Journal (ISSN 2304-6767).

Deadline for manuscript submissions: 31 January 2026 | Viewed by 10913

Special Issue Editors

Dr. Saverio Capodiferro

E-Mail Website
Guest Editor
Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70124 Bari, Italy
Interests: dental prosthesis; oral pathology; implant dentistry; dental materials; dentures
Special Issues, Collections and Topics in MDPI journals
Dr. Massimo Corsalini

E-Mail Website
Guest Editor
Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70124 Bari, Italy
Interests: oral medicine; oral surgical pathology; laser application in oral surgical pathology; implant rehabilitation of oncologic patients or those affected by oral mucosa/jaw diseases
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe D'Albis

E-Mail Website
Guest Editor Assistant
Department of Interdisciplinary Medicine, University of Bari Al-do Moro, 70124 Bari, Italy
Interests: dental prosthesis; dental materials; periodontology; zirconia; dental implants; oral surgery; minimal invasive dentistry; bone regeneration; digital dentistry

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is transforming digital dentistry, bringing unmatched innovation in diagnostics, treatment planning, and execution. By integrating AI-enabled tools in prosthodontics and oral surgery, clinicians are able to achieve higher accuracy, predictability, and efficiency in oral rehabilitation. AI-enabled innovations are transforming the evaluation of occlusion, prosthetic design, and optimization of surgical procedures among dental professionals, with a guarantee of better patient outcomes and optimized workflow.

This Special Issue will cover current applications of AI in oral rehabilitation, encompassing a wide range of topics, including the following:

  • AI in computer-aided occlusal surface modeling and digital prosthodontics.
  • Artificial intelligence-powered occlusion assessment and bite alignment analysis.
  • Automated identification of anatomical structures for precise diagnosis.
  • AI-assisted implant positioning and bone quality assessment.
  • AI-driven optimization of surgical and prosthetic interventions.
  • AI-assisted treatment planning and workflow automation.

By combining cutting-edge research and expert opinions, this Special Issue seeks to provide important insights regarding the revolutionary influence of artificial intelligence on modern dentistry. We invite researchers and healthcare professionals to contribute original research, in-depth reviews, and case studies that focus on the applied applications and future directions of AI in oral rehabilitation.

We look forward to your submissions.

Dr. Saverio Capodiferro
Dr. Massimo Corsalini
Guest Editors

Dr. Giuseppe D'Albis
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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. Dentistry Journal is an international peer-reviewed open access monthly 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 2000 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 rehabilitation
  • prosthodontics
  • artificial intelligence
  • implant dentistry
  • dentures
  • digital dentistry

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

Published Papers (5 papers)

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16 pages, 2780 KB  
Article
Multi-Class Malocclusion Detection on Standardized Intraoral Photographs Using YOLOv11
by Ani Nebiaj, Markus Mühling, Bernd Freisleben and Babak Sayahpour
Dent. J. 2026, 14(1), 60; https://doi.org/10.3390/dj14010060 - 16 Jan 2026
Viewed by 128
Abstract
Background/Objectives: Accurate identification of dental malocclusions from routine clinical photographs can be time-consuming and subject to interobserver variability. A YOLOv11-based deep learning approach is presented and evaluated for automatic malocclusion detection on routine intraoral photographs, testing the hypothesis that training on a structured [...] Read more.
Background/Objectives: Accurate identification of dental malocclusions from routine clinical photographs can be time-consuming and subject to interobserver variability. A YOLOv11-based deep learning approach is presented and evaluated for automatic malocclusion detection on routine intraoral photographs, testing the hypothesis that training on a structured annotation protocol enables reliable detection of multiple clinically relevant malocclusions. Methods: An anonymized dataset of 5854 intraoral photographs (frontal occlusion; right/left buccal; maxillary/mandibular occlusal) was labeled according to standardized instructions derived from the Index of Orthodontic Treatment Need (IOTN) A total of 17 clinically relevant classes were annotated with bounding boxes. Due to an insufficient number of examples, two malocclusions (transposition and non-occlusion) were excluded from our quantitative analysis. A YOLOv11 model was trained with augmented data and evaluated on a held-out test set using mean average precision at IoU 0.5 (mAP50), macro precision (macro-P), and macro recall (macro-R). Results: Across 15 analyzed classes, the model achieved 87.8% mAP50, 76.9% macro-P, and 86.1% macro-R. The highest per-class AP50 was observed for Deep bite (98.8%), Diastema (97.9%), Angle Class II canine (97.5%), Anterior open bite (92.8%), Midline shift (91.8%), Angle Class II molar (91.1%), Spacing (91%), and Crowding (90.1%). Moderate performance included Anterior crossbite (88.3%), Angle Class III molar (87.4%), Head bite (82.7%), and Posterior open bite (80.2%). Lower values were seen for Angle Class III canine (76%), Posterior crossbite (75.6%), and Big overjet (75.3%). Precision–recall trends indicate earlier precision drop-off for posterior/transverse classes and comparatively more missed detections in Posterior crossbite, whereas Big overjet exhibited more false positives at the chosen threshold. Conclusion: A YOLOv11-based deep learning system can accurately detect several clinically salient malocclusions on routine intraoral photographs, supporting efficient screening and standardized documentation. Performance gaps align with limited examples and visualization constraints in posterior regions. Larger, multi-center datasets, protocol standardization, quantitative metrics, and multimodal inputs may further improve robustness. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Rehabilitation)
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9 pages, 394 KB  
Article
Evaluation of Chatbot Responses to Text-Based Multiple-Choice Questions in Prosthodontic and Restorative Dentistry
by Reinhard Chun Wang Chau, Khaing Myat Thu, Ollie Yiru Yu, Richard Tai-Chiu Hsung, Denny Chon Pei Wang, Manuel Wing Ho Man, John Junwen Wang and Walter Yu Hang Lam
Dent. J. 2025, 13(7), 279; https://doi.org/10.3390/dj13070279 - 21 Jun 2025
Cited by 12 | Viewed by 1403
Abstract
Background/Objectives: This study aims to evaluate the response accuracy and quality of three AI chatbots—GPT-4.0, Claude-2, and Llama-2—in answering multiple-choice questions in prosthodontic and restorative dentistry. Methods: A total of 191 text-based multiple-choice questions were selected from the prosthodontic and restorative [...] Read more.
Background/Objectives: This study aims to evaluate the response accuracy and quality of three AI chatbots—GPT-4.0, Claude-2, and Llama-2—in answering multiple-choice questions in prosthodontic and restorative dentistry. Methods: A total of 191 text-based multiple-choice questions were selected from the prosthodontic and restorative dentistry sections of the United States Integrated National Board Dental Examination (INBDE) (n = 80) and the United Kingdom Overseas Registration Examination (ORE) (n = 111). These questions were inputted into the chatbots, and the AI-generated answers were compared with the official answer keys to determine their accuracy. Additionally, two dental specialists independently evaluated the rationales accompanying each chatbot response for accuracy, relevance, and comprehensiveness, categorizing them into four distinct ratings. Chi-square and post hoc Z-tests with Bonferroni adjustment were used to analyze the responses. The inter-rater reliability for evaluating the quality of the rationale ratings among specialists was assessed using Cohen’s Kappa (κ). Results: GPT-4.0 (65.4%; n = 125/191) demonstrated a significantly higher proportion of correctly answered multiple-choice questions when compared to Claude-2 (41.9%; n = 80/191) (p < 0.017) and Llama-2 (26.2%; n = 50/191) (p < 0.017). Significant differences were observed in the answer accuracy among all of the chatbots (p < 0.001). In terms of the rationale quality, GPT-4.0 (58.1%; n = 111/191) had a significantly higher proportion of “Correct Answer, Correct Rationale” than Claude-2 (37.2%; n = 71/191) (p < 0.017) and Llama-2 (24.1%; n = 46/191) (p < 0.017). Significant differences were observed in the rationale quality among all of the chatbots (p < 0.001). The inter-rater reliability was very high (κ = 0.83). Conclusions: GPT-4.0 demonstrated the highest accuracy and quality of reasoning in responding to prosthodontic and restorative dentistry questions. This underscores the varying efficacy of AI chatbots within specialized dental contexts. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Rehabilitation)
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14 pages, 912 KB  
Article
Evaluation of Large Language Model Performance in Answering Clinical Questions on Periodontal Furcation Defect Management
by Georgios S. Chatzopoulos, Vasiliki P. Koidou, Lazaros Tsalikis and Eleftherios G. Kaklamanos
Dent. J. 2025, 13(6), 271; https://doi.org/10.3390/dj13060271 - 18 Jun 2025
Cited by 6 | Viewed by 1240
Abstract
Background/Objectives: Large Language Models (LLMs) are artificial intelligence (AI) systems with the capacity to process vast amounts of text and generate human-like language, offering the potential for improved information retrieval in healthcare. This study aimed to assess and compare the evidence-based potential [...] Read more.
Background/Objectives: Large Language Models (LLMs) are artificial intelligence (AI) systems with the capacity to process vast amounts of text and generate human-like language, offering the potential for improved information retrieval in healthcare. This study aimed to assess and compare the evidence-based potential of answers provided by four LLMs to common clinical questions concerning the management and treatment of periodontal furcation defects. Methods: Four LLMs—ChatGPT 4.0, Google Gemini, Google Gemini Advanced, and Microsoft Copilot—were used to answer ten clinical questions related to periodontal furcation defects. The LLM-generated responses were compared against a “gold standard” derived from the European Federation of Periodontology (EFP) S3 guidelines and recent systematic reviews. Two board-certified periodontists independently evaluated the answers for comprehensiveness, scientific accuracy, clarity, and relevance using a predefined rubric and a scoring system of 0–10. Results: The study found variability in LLM performance across the evaluation criteria. Google Gemini Advanced generally achieved the highest average scores, particularly in comprehensiveness and clarity, while Google Gemini and Microsoft Copilot tended to score lower, especially in relevance. However, the Kruskal–Wallis test revealed no statistically significant differences in the overall average scores among the LLMs. Evaluator agreement and intra-evaluator reliability were high. Conclusions: While LLMs demonstrate the potential to answer clinical questions related to furcation defect management, their performance varies. LLMs showed different comprehensiveness, scientific accuracy, clarity, and relevance degrees. Dental professionals should be aware of LLMs’ capabilities and limitations when seeking clinical information. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Rehabilitation)
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14 pages, 826 KB  
Systematic Review
Current Applications of Chatbots Powered by Large Language Models in Oral and Maxillofacial Surgery: A Systematic Review
by Vincenzo Ronsivalle, Simona Santonocito, Umberto Cammarata, Eleonora Lo Muzio and Marco Cicciù
Dent. J. 2025, 13(6), 261; https://doi.org/10.3390/dj13060261 - 11 Jun 2025
Cited by 2 | Viewed by 1772
Abstract
Background/Objectives: In recent years, interest has grown in the clinical applications of artificial intelligence (AI)-based chatbots powered by large language models (LLMs) in oral and maxillofacial surgery (OMFS). However, there are conflicting opinions regarding the accuracy and reliability of the information they provide, [...] Read more.
Background/Objectives: In recent years, interest has grown in the clinical applications of artificial intelligence (AI)-based chatbots powered by large language models (LLMs) in oral and maxillofacial surgery (OMFS). However, there are conflicting opinions regarding the accuracy and reliability of the information they provide, raising questions about their potential role as support tools for both clinicians and patients. This systematic review aims to analyze the current literature on the use of conversational agents powered by LLMs in the field of OMFS. Methods: The review was conducted following PRISMA guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. Original studies published between 2023 and 2024 in peer-reviewed English-language journals were included. Sources were identified through major electronic databases, including PubMed, Scopus, Google Scholar, and Web of Science. The risk of bias in the included studies was assessed using the ROBINS-I tool, which evaluates potential bias in study design and conduct. Results: A total of 49 articles were identified, of which 4 met the inclusion criteria. One study showed that ChatGPT provided the most accurate responses compared to Microsoft Copilot (ex-Bing) and Google Gemini (ex-Bard) for questions related to OMFS. Other studies highlighted that ChatGPT-4 can assist surgeons with quick and relevant information, though responses may vary depending on the quality of the questions. Conclusions: Chatbots powered by LLMs can enhance efficiency and decision-making in OMFS routine clinical cases. However, based on the limited number of studies included in this review (four), their performance remains constrained in complex clinical scenarios and in managing emotionally sensitive patient interactions. Further research on clinical validation, prompt formulation, and ethical oversight is essential to safely integrating LLM technologies into OMFS practices. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Rehabilitation)
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18 pages, 1968 KB  
Systematic Review
Immediate Loading of Implants-Supported Fixed Partial Prostheses in Posterior Regions: A Systematic Review
by Giuseppe D’Albis, Marta Forte, Abdulrahman Omar Alrashadah, Lorenzo Marini, Massimo Corsalini, Andrea Pilloni and Saverio Capodiferro
Dent. J. 2025, 13(5), 213; https://doi.org/10.3390/dj13050213 - 15 May 2025
Cited by 4 | Viewed by 5475
Abstract
Background: Modern dentistry strives to achieve increasingly less invasive procedures as the ultimate therapeutic goal. The careful selection of suitable candidates for immediate dental implants can offer an opportunity to reduce treatment time, lower the relative costs and improve overall patient satisfaction. [...] Read more.
Background: Modern dentistry strives to achieve increasingly less invasive procedures as the ultimate therapeutic goal. The careful selection of suitable candidates for immediate dental implants can offer an opportunity to reduce treatment time, lower the relative costs and improve overall patient satisfaction. Methods: A systematic search was conducted in March 2025, without any time restrictions, in Medline, Pubmed and Web of Science databases. To identify other related references, further research was performed. Articles related to current knowledge about the immediate loading of dental implants supporting fixed partial prosthesis in the posterior region were included. Articles not available in abstract form and articles not published in the English language were excluded. Results: A total of ten studies were eligible for inclusion in the current study. The search strategy resulted in a survival rate ranging from 86% to 100%, and a failure rate of less than 21.6%, with a mean follow-up of 55.6 months. Statistical analysis revealed no significant differences in survival rates between implants placed in the maxilla and mandible (χ2 = 0.42, p = 0.81, df = 2). Follow-up varied from one to ten years, reflecting variability both in study design and duration. Conclusions: The selected studies highlight the heterogeneity in immediate loading protocols for implant-supported fixed partial prosthesis in the posterior regions, emphasizing the variability in prosthetic materials and implant types, suggesting that immediate loading is a reliable, patient-centered therapeutic option with favorable long-term outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oral Rehabilitation)
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