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Artificial Intelligence (AI) in Dental Clinical Practice

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A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Dentistry, Oral Surgery and Oral Medicine".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 6491

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Editors

Prof. Dr. Eiji Tanaka

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Guest Editor

Department of Orthodontics and Dentofacial Orthopedics, Tokushima University Graduate School of Biomedical Sciences, Tokushima 770-8504, Japan
Interests: clinical orthodontics; skeletal anchorage; condylar resorption; temporomandibular disorders; oral–maxillofacial regeneration; therapeutic ultrasound
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Dr. Keiichiro Watanabe

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Department of Orthodontics and Dentofacial Orthopedics, Tokushima University Graduate School of Biomedical Sciences, Tokushima 770-8504, Japan
Interests: digital dentistry; AI; resin composite; clinical orthodontics

Special Issue Information

Dear Colleagues,

Background: Artificial intelligence (AI) has become increasingly integrated into various aspects of society, including healthcare. In dentistry, the adoption of AI technologies is gradually gaining recognition for their potential to enhance diagnostic accuracy, streamline clinical workflows, and support personalized treatment plans based on large-scale data analysis.

Aim and scope: This Special Issue aims to provide a scientific forum for exploring and critically evaluating the application of AI in dental clinical practice. We particularly emphasize evidence-based validation and the need to ensure transparency, reliability, and accuracy in AI-driven systems used for clinical examination, diagnosis, and treatment planning.

History: Although AI has already shown significant progress in general medicine—such as radiology and pathology—its implementation in dentistry remains in an early but rapidly evolving stage. The recent emergence of machine learning, deep learning, and advanced image-processing techniques has led to a dramatic increase in the number of AI tools, many of which still require robust clinical evaluation.

We encourage submissions that address both technological innovation and clinical relevance, particularly studies on automated diagnostic algorithms, AI-driven radiographic interpretation, and real-time clinical decision support. Contributions analyzing the ethical, legal, and social implications of using AI in dental settings are also highly welcome.

We welcome high-quality original research articles, comprehensive reviews, and detailed case studies that rigorously examine AI technologies across all dental specialties. Submissions should highlight the limitations of AI decision-making, the integrity and transparency of training datasets, and the essential role of clinician oversight in AI-assisted care.

Prof. Dr. Eiji Tanaka
Dr. Keiichiro Watanabe
Guest Editors

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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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

artificial intelligence
dental diagnostics
machine learning
deep learning
digital dentistry
clinical decision support
diagnostic automation
data transparency
evidence-based dentistry

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Published Papers (6 papers)

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Research

15 pages, 1702 KB

Open AccessArticle

Automated YOLO-Based Cephalometric Landmark Detection for ANB-Based Skeletal Classification: A Retrospective Single-Centre Study

by Jacek Kotula, Marcin Konarzewski, Jakub Polkowski, Krzysztof Kotula, Joanna Lis, Rafal Porowski, Anna Ewa Kuc, Beata Kawala and Michal Sarul

J. Clin. Med. 2026, 15(13), 5149; https://doi.org/10.3390/jcm15135149 (registering DOI) - 2 Jul 2026

Viewed by 287

Abstract

Background/Objectives: Automated cephalometric landmark detection using deep learning has the potential to streamline routine orthodontic diagnosis. However, the clinical relevance of artificial intelligence (AI) localisation accuracy depends on how detection errors propagate into derived angular measurements and skeletal classifications. We retrospectively evaluated 14 YOLO-based model configurations and quantified the agreement between AI-derived and expert-derived ANB-based skeletal classifications. Methods: Twelve working YOLO-based models (YOLOv5xu, YOLOv11 nano/small/medium/large variants) were trained on a single-centre dataset of 120 lateral cephalograms and evaluated on an independent test set of 11 cephalograms (stratified across skeletal Classes I, II, III). The four ANB-defining landmarks (Sella, Nasion, A-point, B-point) were the focus of the analysis. Each test cephalogram had been annotated by four orthodontists (44 measurements per image), yielding the expert reference. We assessed the effects of architecture, bounding-box size (40/100/150 px), training dataset scale (235–4255 images) and training epochs on localisation accuracy (mean radial error, MRE; Successful Detection Rate, SDR) and on the downstream ANB-based skeletal classification. Diagnostic concordance was quantified by classification agreement, Cohen’s

κ

with bootstrap 95% confidence intervals (10,000 iterations), an exact one-sided binomial test for discordance, and Wilson exact CIs per class. Results: The best-performing model (Model 2; YOLOv11l, 40 × 40 px bounding box, 1175 training images) achieved an MRE of

3.10 \pm 1.00

mm and a SDR@4 mm of 87.2% for S, N, A, and B. ANB-based skeletal classification demonstrated 96.9% concordance with expert assessments (95% bootstrap CI: 93.8–99.2%; Cohen’s

κ

= 0.946 [95% CI 0.89–0.99]; exact binomial test against a 90% concordance threshold

p = 0.003

). Per-class concordance was Class I 95.8% (23/24), Class II 94.9% (56/59), and Class III 100% (47/47). Three of four discordant cases clustered near the Class I/II diagnostic threshold (expert ANB

\approx 4.5 °

). Bounding-box size dominated localisation accuracy, with a 3.5-fold increase in MRE from 40 × 40 to 150 × 150 px configurations and SDR@4 mm collapsing from 82.8% to 0%. Conclusions: Within the constraints of a retrospective single-centre design with a small (n = 11) independent test set, YOLO-based AI landmark detection demonstrated promising diagnostic concordance with expert consensus for ANB-based skeletal classification. These findings warrant prospective, multi-centre external validation before clinical deployment and support a confidence-aware workflow in which AI predictions for borderline ANB values undergo mandatory clinician verification. Bounding-box calibration emerged as the single most impactful preprocessing decision. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Dental Clinical Practice)

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14 pages, 1161 KB

Open AccessArticle

Evaluating the Ability of Multimodal Artificial Intelligence to Identify Endodontic Instruments: A Comparative Study of ChatGPT-4o and Gemini 3 Flash

by Samet Tosun and Emre Çulha

J. Clin. Med. 2026, 15(11), 4391; https://doi.org/10.3390/jcm15114391 - 5 Jun 2026

Viewed by 268

Abstract

Background/Objectives: Multimodal large language models (LLMs) are increasingly integrated into dental diagnostics. This study evaluated the ability of ChatGPT-4o and Gemini 3 Flash to visually identify endodontic instruments and assess their explanatory plausibility regarding instrument morphology. Methods: Standardized images of five endodontic file systems (Reciproc R25, Reciproc Blue, WaveOne Gold, MM One Shape, and XP-endo Finisher) were submitted to both models via their free tiers. Each image was evaluated 50 times per model (total n = 500) to assess both classification accuracy and response consistency. Visual recognition performance was measured using recall, precision, and F1-score, while the plausibility of morphological explanations was evaluated using a structured 3-point scale. Results: Gemini 3 Flash demonstrated significantly higher recognition performance compared to ChatGPT-4o (p < 0.001). The overall acceptable response rate was higher for Gemini 3 Flash (94.4%, [95% CI: 91.5–97.3%]) than for ChatGPT-4o (67.2%, [95% CI: 61.4–73.0%]; p < 0.001). Notably, Gemini 3 Flash showed strong performance in identifying complex instrument designs, whereas ChatGPT-4o exhibited marked limitations in recognizing certain non-standard geometries. Reliability analysis indicated higher consistency for Gemini 3 Flash (κ = 0.86, [95% CI: 0.81–0.91]) compared to ChatGPT-4o (κ = 0.51, [95% CI: 0.44–0.58]). Conclusions: Gemini 3 Flash outperformed ChatGPT-4o in both classification accuracy and consistency in this controlled visual identification task. While these findings highlight the potential of multimodal LLMs in endodontic workflows, their current performance variability limits direct, autonomous clinical application. Further validation under clinically realistic conditions is required before such systems can be considered reliable adjunctive tools. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Dental Clinical Practice)

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11 pages, 968 KB

Open AccessArticle

Deep Learning-Assisted Localization of Cystic Lesions and Benign Tumors in the Maxillofacial Region Using Panoramic Radiographs: A Preliminary Feasibility Study

by Kai-Hua Lien, Sih-Yi Wu, Yun-Ya Yang, Jia-Yu Liu, Yi-Cheng Chen, Ten-Yi Huang, Yu-Wen Tang, Yen-Chu Hsiao, Chung-Bin Wu and Cheng-Chia Yu

J. Clin. Med. 2026, 15(7), 2784; https://doi.org/10.3390/jcm15072784 - 7 Apr 2026

Viewed by 549

Abstract

Background/Objectives: Automated localization of cystic lesions and benign tumors on panoramic radiographs may support lesion recognition in the maxillofacial region. This preliminary feasibility study aimed to develop and evaluate a deep learning model based on Mask R-CNN for the localization of dentigerous cysts (DCs), radicular cysts (RCs), odontogenic keratocysts (OKCs), and ameloblastomas using panoramic radiographs. Methods: A total of 215 panoramic radiographs were retrospectively collected from Taichung Veterans General Hospital (2018–2023). After excluding postoperative, recurrent, or low-quality images, 184 lesions were allocated to the training set and 47 lesions to the testing set. Lesions were annotated based on pathology-confirmed diagnoses. The Mask R-CNN model was trained to localize and classify four lesion types. Model performance was evaluated using precision, sensitivity (recall), and F1 score at an Intersection over Union (IoU) threshold of 0.1. Results: In the testing set (n = 47), 26 lesions were correctly localized, yielding an overall sensitivity of 55.3% and a precision of 83.9%. The corresponding F1 score was 66.7%. Lesion-specific sensitivities were 40.0% for ameloblastomas, 37.5% for OKCs, 36.8% for RCs, and 93.3% for DCs. Conclusions: This study suggests the preliminary feasibility of a deep learning-assisted approach for lesion localization on panoramic radiographs. However, the absence of lesion-free control images and the limited dataset size restrict the generalizability and clinical applicability of the findings. Further validation using larger and more balanced datasets is required. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Dental Clinical Practice)

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15 pages, 1156 KB

Open AccessArticle

CBCT-Based Orthodontic Classification Using Commercial AI: Completeness and Accuracy in Independent Validation

by Natalia Kazimierczak, Nora Sultani, Szymon Krzykowski, Zbigniew Serafin and Wojciech Kazimierczak

J. Clin. Med. 2026, 15(4), 1637; https://doi.org/10.3390/jcm15041637 - 21 Feb 2026

Cited by 1 | Viewed by 696

Abstract

Background/Objectives: Artificial intelligence (AI) tools for orthodontic diagnosis are increasingly used in clinical practice; however, there is limited evidence regarding their performance in CBCT-based assessments. In this study, we evaluated the diagnostic reliability of the Diagnocat platform for categorical orthodontic diagnoses obtained from CBCT examinations. Methods: Fifty-nine patients who underwent large-field CBCT (13 × 16 cm) and lateral cephalograms within 30 days were included, and CBCT scans were processed using Diagnocat (v1.0). The platform’s categorical outputs—sagittal skeletal class, vertical facial pattern, overbite category, and Dental Angle class—were compared with manual cephalometric analyses performed by an experienced orthodontist (reference standard). Standard thresholds were used to convert reference continuous measurements into categorical variables. Missing or ‘N/A’ index test outputs were treated as diagnostic failures in accordance with STARD recommendations. Agreement was assessed via Cohen’s kappa (κ), and the sensitivity, specificity, PPV, and NPV were calculated for angle classification. Results: The AI platform generated skeletal and vertical classifications in only 3/59 (5%) and 1/59 (1.7%) patients, respectively. Agreement was fair (κ = 0.324) for overbite categorization, and the Dental Angle class was provided for 34/59 (57.6%) patients. When “N/A” results were treated as diagnostic failures, the overall system usability was <10% for skeletal parameters. Conclusions: The platform demonstrated insufficient diagnostic reliability and failed to generate outputs for most patients. While the specificities for generated diagnoses were acceptable, the low data completeness rate renders the tool currently unsuitable for independent clinical decision-making. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Dental Clinical Practice)

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16 pages, 3051 KB

Open AccessArticle

Automated Classification of Enamel Caries from Intraoral Images Using Deep Learning Models: A Diagnostic Study

by Faris Yahya I. Asiri

J. Clin. Med. 2025, 14(24), 8959; https://doi.org/10.3390/jcm14248959 - 18 Dec 2025

Cited by 5 | Viewed by 2514

Abstract

Background: Dental caries is a prevalent global oral health issue. The early detection of enamel caries, the initial stage of decay, is critical to preventive dentistry but is often limited by the subjectivity and variability of conventional diagnostic methods. Objective: This study aims to develop and evaluate two explainable deep learning models for the automated classification of enamel caries from intraoral images. Dataset and Methodology: A publicly available dataset of 2000 intraoral images showing early-stage enamel caries, advanced enamel caries, no-caries was used. The dataset was split into training, validation, and test sets in a 70:15:15 ratio, and data preprocessing and augmentation were applied to the training set to balance the dataset and prevent model overfitting. Two models were developed, ExplainableDentalNet, a custom lightweight CNN, and Interpretable ResNet50-SE, a fine-tuned ResNet50 model with Squeeze-and-Excitation blocks, and both were integrated with Gradient-Weighted Class Activation Mapping (Grad-CAM) for visual interpretability. Results: As evaluated on the test set, ExplainableDentalNet achieved an overall accuracy of 96.66% and a Matthews Correlation Coefficient [MCC] = 0.95, while Interpretable ResNet50-SE achieved 98.30% accuracy (MCC = 0.975). McNemar’s test indicated no significant prediction bias, with p > 0.05, and internal bootstrap and cross-validation analyses indicated stable performance. Conclusions: The proposed explainable models demonstrated high diagnostic accuracy in enamel caries classification on the studied dataset. While the present findings are promising, future clinical applications will require external validation on multi-center datasets. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Dental Clinical Practice)

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9 pages, 6062 KB

Open AccessArticle

Age Estimation from Lateral Cephalograms Using Deep Learning: A Pilot Study from Early Childhood to Older Adults

by Ryohei Tokinaga, Yuichi Mine, Yuki Yoshimi, Shota Okazaki, Shota Ito, Saori Takeda, Saki Ogawa, Tzu-Yu Peng, Naoya Kakimoto, Kotaro Tanimoto and Takeshi Murayama

J. Clin. Med. 2025, 14(19), 7084; https://doi.org/10.3390/jcm14197084 - 7 Oct 2025

Cited by 2 | Viewed by 1153

Abstract

Background/Objectives: The purpose of this study is twofold: first, to construct and evaluate a deep-learning model for automated age estimation from lateral cephalograms spanning early childhood to older adulthood; and second, to determine whether sex-specific training improves predictive accuracy. Methods: This retrospective study examined 600 lateral cephalograms (ages 4–63 years; 300 female, 300 male). The images were randomly divided into five cross-validation folds, stratified by sex and age. An ImageNet-pretrained DenseNet-121 was employed for age regression. Three networks were trained: mixed-sex, female-only, and male-only. Performance was evaluated using mean absolute error (MAE) and the coefficient of determination (R²). Grad-CAM heatmaps quantified the contributions of six craniofacial regions. Duplicate patients were excluded to minimize sampling bias. Results: The mixed-sex model achieved an MAE of 2.50 ± 0.27 years, an R² of 0.84 ± 0.04, the female-only model achieved an MAE of 3.04 ± 0.37 years and an R² of 0.82 ± 0.04, and the male-only model achieved an MAE of 2.29 ± 0.27 years and an R² of 0.83 ± 0.04. Grad-CAM revealed dominant activations over the frontal bone in the mixed-sex model; the occipital bone and cervical soft tissue in the female model; and the parietal bone in the male model. Conclusions: A DenseNet-121-based analysis of lateral cephalograms can provide a clinically relevant age estimation with an error margin of approximately ±2.5 years. Using male-only model slightly improves performance metrics, and careful attention to training data distribution is crucial for broad applicability. Our findings suggest a potential contribution to forensic age estimation, growth and development research, and support for unidentified deceased individuals when dental records are unavailable. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Dental Clinical Practice)

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