EBD spotlight: Artificial intelligence for dental imaging

Artificial intelligence (AI) has garnered significant attention in recent years for its potential to revolutionize healthcare, including dentistry. However, despite the growing body of literature on AI-based dental image analysis, challenges such as the integration of AI into clinical workflows, variability in dataset quality, and the lack of standardized evaluation metrics remain largely underexplored. This systematic review aims to address these gaps by assessing the extent to which AI technologies have been integrated into dental specialties, with a specific focus on their applications in dental imaging. A comprehensive review of the literature was conducted, selecting relevant studies through electronic searches from Scopus, Google Scholar, and PubMed databases, covering publications from 2018 to 2023. A total of 52 articles were systematically analyzed to evaluate the diverse approaches of machine learning (ML) and deep learning (DL) in dental imaging. This review reveals that AI has become increasingly prevalent, with researchers predominantly employing convolutional neural networks (CNNs) for detection and diagnosis tasks. Pretrained networks demonstrate strong performance in many scenarios, while ML techniques have shown growing utility in estimation and classification. Key challenges identified include the need for larger, annotated datasets and the translation of research outcomes into clinical practice. The findings underscore AI’s potential to significantly advance diagnostic support, particularly for non-specialist dentists, improving patient care and clinical efficiency. AI-driven software can enhance diagnostic accuracy, facilitate data sharing, and support collaboration among dental professionals. Future developments are anticipated to enable patient-specific optimization of restoration designs and implant placements, leveraging personalized data such as dental history, tissue type, and bone thickness to achieve better outcomes.

This scoping review aimed to answer the question: to what extent do artificial intelligence applications in dental and orthopedic skeletal imaging demonstrate true cross-disciplinary methodological convergence versus parallel development with shared translational barriers? This scoping review synthesizes AI applications across both fields to characterize methodological overlap, developmental asymmetries, and translational gaps, rather than assuming convergence. Following the PRISMA-ScR reporting standards, we searched PubMed, Scopus, Web of Science, IEEE Xplore, and EMBASE for peer-reviewed, English-language human studies published between January 2015 and May 2025. Eligible studies applied AI, machine learning, or deep learning to diagnostic, segmentation, or preoperative planning tasks in dental or orthopedic imaging. Three reviewers independently extracted data on imaging modality, task, model architecture, dataset characteristics, validation strategy, performance metrics, and translational considerations, with random auditing for consistency. Fifty-nine studies met inclusion criteria, comprising 48 dental (81.36%) and 11 orthopedic (18.64%) investigations, with no study spanning both domains. Most applications focused on foundational tasks such as segmentation and detection/classification using two-dimensional radiographs and cone-beam computed tomography. Computed tomography primarily supported bony anatomy and preoperative planning, while magnetic resonance imaging, the EOS system, and intraoral scanners were used in specialized workflows. Convolutional neural networks, particularly U-Net/nnU-Net variants and EfficientNet/ResNet backbones with YOLO-based detectors, dominated, alongside emerging transformer-based and hybrid physics-informed approaches. Internal validation performance was frequently high for segmentation (typical Dice 0.90-0.99), while more complex or anatomically challenging targets showed lower and more variable performance. External validation, prospective evaluation, and standardized reporting of calibration, expert comparators, and demographic performance were uncommon. The current AI skeletal imaging literature demonstrates strong technical feasibility but uneven clinical maturity, with dental imaging dominating in volume and automation of foundational tasks and orthopedic applications remaining fewer, more heterogeneous, and less mature. Rather than evidencing true cross-disciplinary convergence, the findings highlight asymmetrical development and shared translational barriers, particularly in validation rigor and real-world integration. By explicitly identifying these asymmetries, this review provides a realistic foundation for future cross-disciplinary collaboration focused on harmonized validation standards, clinically meaningful benchmarks, and equitable, workflow-native deployment.

Dental caries pose a significant public health concern, affecting a vast population globally. Traditional clinical examination methods, although reliable, can be subject to human error and time-consuming. Artificial intelligence (AI) technologies have emerged as promising tools to enhance diagnostic accuracy and efficiency. This study explores the potential of AI in revolutionizing dental caries detection. Materials and Methods: A cohort of 50 patients with varying degrees of dental caries participated in this comparative analysis. Clinical examination by dental professionals served as the gold standard for caries detection. AI algorithms were trained using dental images, and their performance was evaluated against the clinical examination results. Results: The AI-based detection system demonstrated a sensitivity of 92% and a specificity of 85% in identifying dental caries, with an overall accuracy of 88%. The clinical examination yielded a sensitivity of 86% and a specificity of 90%, resulting in an overall accuracy of 88%. Statistical analysis indicated no significant difference between AI-based detection and clinical examination (P > 0.05). Conclusion: AI technology exhibits promise as an adjunctive tool for dental practitioners, potentially reducing diagnostic errors and improving efficiency. Integrating AI into routine dental practice may aid in early caries detection and promote better oral health outcomes.

Proximal caries remains challenging to diagnose using conventional clinical and radiographic methods due to limited visibility, interexaminer variability, and frequent misinterpretation of radiographs. Artificial intelligence (AI), particularly convolutional neural networks (CNNs), has emerged as a promising tool to enhance diagnostic accuracy and efficiency in dental imaging. This systematic review aims to evaluate the diagnostic performance, reliability, and cost-effectiveness of AI-based models, specifically CNNs, in detecting proximal caries compared with conventional diagnostic approaches. A comprehensive search was conducted in PubMed, ScienceDirect, and Google Scholar for studies published between 2014 and 2024 using the keyword “Proximal Caries Detection AND Artificial Intelligence.” Studies involving human subjects and the application of CNNs for proximal caries detection on radiographic images were included. Quality and risk of bias were assessed using the Newcastle–Ottawa Scale. Data extraction covered study design, population characteristics, AI models used, diagnostic outcomes, and performance metrics. Eight studies met the inclusion criteria. Various CNN architectures were identified, including YOLOv3/5, EfficientDet, RetinaNet (ResNet50), VGG19, U-Net ensembles, AssistDent, and ResNet variants. AI models consistently demonstrated higher or comparable diagnostic accuracy to clinicians, with several models achieving superior sensitivity for enamel-only and dentin lesions. YOLOv5 achieved the highest overall performance among compared architectures. AI assistance improved dentists’ detection rates in some studies, although false-positive findings remained a challenge. Cost-effectiveness analyses indicated that AI reduces the number of undetected lesions and may lower long-term restorative treatment costs. AI-based CNN models show strong potential as complementary tools for detecting proximal caries, offering improved diagnostic accuracy, greater consistency, and enhanced efficiency compared with conventional methods. Although they cannot replace clinician judgment, AI systems can support clinical decision-making, facilitate early detection, and potentially reduce the economic burden associated with delayed diagnosis. Further refinement of training datasets and standardized evaluation protocols is needed to optimize clinical integration.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Artificial intelligence (AI) has become an increasingly valuable tool in dental imaging, particularly for the analysis of radiographs and cone-beam computed tomography (CBCT) scans. This paper reviews the current applications and effectiveness of AI algorithms, especially deep learning models, in detecting dental pathologies and anatomical structures. The integration of AI enhances diagnostic accuracy, reduces human error, and streamlines clinical workflows. Despite challenges such as dataset limitations and the need for standardized protocols, AI demonstrates substantial potential to transform dental diagnostics. Continued research and development are essential for optimizing AI tools and promoting their widespread adoption in clinical practice.

Artificial intelligence for osteoporosis detection on panoramic radiography: A systematic review and meta analysis.

Background: This study investigates the diagnostic reliability of an artificial intelligence (AI)-based software (Diagnocat) in caries, dental restorations, missing teeth, and periodontal bone loss on panoramic radiographs (PRs), comparing its performance with evaluations from three independent dental experts serving as ground truth. Methods: A total of 104 PRs were analyzed using Diagnocat, which assigned a likelihood score (0–100%) for each condition. The same images were independently evaluated by three experts. The diagnostic performance of Diagnocat was evaluated using sensitivity, specificity, and receiver operating characteristic (ROC) curve analysis, while inter-rater agreement was assessed through Cohen’s kappa (κ). Results: Diagnocat showed high overall sensitivity (99.2%), identifying nearly all conditions marked as present by human evaluators. Specificity was low (8.7%), indicating a tendency to overdiagnose. Overall accuracy was 96%, likely influenced by the coexistence of multiple conditions. Sensitivity ranged from 77% to 96%, while specificity varied: dental restorations (66%), missing teeth (68%), periodontal bone loss (71%), and caries signs (47%). The agreement was fair for dental restorations (κ = 0.39) and missing teeth (κ = 0.37), but poor for caries signs (κ = −0.15) and periodontal bone loss (κ = −0.62). Conclusions: Diagnocat shows promise as a screening tool due to its high sensitivity, but low specificity and poor agreement for certain conditions warrant cautious interpretation alongside clinical evaluation.

State-of-art technologies, challenges, and emerging trends of computer vision in dental images

X-ray imaging plays a pivotal role in clinical diagnostics by facilitating tasks ranging from organ segmentation to disease detection. With the integration of artificial intelligence (AI), the capabilities of X-ray analysis have been greatly enhanced. However, acquiring a comprehensive dataset for training AI models remains challenging due to various factors including patient privacy, radiation exposure, and cost constraints. To overcome this hurdle, researchers have turned to create synthetic X-ray datasets. In dental imaging, the synthesis of Panoramic X-rays (PXs) poses unique challenges owing to the intricate nature of the oral cavity and the inherent diversity among patients. Existing efforts predominantly rely on Cone Beam Computed Tomography (CBCT) or phantoms for such tasks. Synthesizing PX can be beneficial when the PX imaging cannot be performed immediately or when dentists need to find a one-to-one correspondence between PX and CBCT. This review paper comprehensively examines the methodologies employed for synthesizing dental PXs, specifically, automated synthesis, elucidating the methods utilized and the associated advantages, limitations, and potential clinical applications in routine practice. By shedding light on these approaches, this review aims to provide valuable insights for researchers and practitioners in the dental imaging domain.

Dental caries, commonly known as tooth decay or cavities, have long posed a significant challenge to oral health. Early identification is essential for effective treatment and for preventing further damage to teeth. Traditionally, dentists relied on visual examinations to diagnose cavities by looking for obvious signs of decay. However, due to the increasing patient-to-dentist ratio and the advancement of artificial intelligence, deep learning techniques have gained prominence in recent years. Despite their potential, the black-box nature of these deep learning models can lead to ambiguity in their decision-making processes. To tackle this issue, a study was conducted using explainable AI in human-centric medical applications to identify cavities in dental images and provide clear visualizations for diagnosis. Our approach integrates Vision Transformer (ViT) with Local Interpretable Model-Agnostic Explanations (LIME), where LIME is utilized to visually elucidate the classifications made by the ViT model. The Vision Transformer effectively extracts relevant features from dental images, while LIME offers interpretable visual explanations for the AI model's decisions, helping dentists understand the factors influencing their diagnoses. Experimental results demonstrate the efficacy of our approach in cavity detection, facilitating early intervention and ultimately improving oral health outcomes through explainable AI.

The integration of artificial intelligence (AI) in dentistry is revolutionizing patient care by enhancing diagnostic accuracy, optimizing treatment planning, and improving surgical precision. Our review highlights AI’s pivotal role in radiology, where advanced deep learning algorithms analyze extensive dental image datasets, facilitating the early detection of conditions such as caries and periodontal disease. This capability leads to timely interventions and improved patient outcomes. In treatment planning, AI synthesizes diverse patient data, enabling personalized recommendations that align with evidence-based protocols, thus fostering a patient-centered approach in dental practices. Moreover, the incorporation of robotics in surgical procedures, particularly in dental implant placements, showcases AI’s potential to minimize human error and enhance procedural accuracy. These systems provide real-time assessments, ensuring that interventions are both effective and comfortable for patients. We also explore the automation of administrative tasks through AI, which streamlines workflows and allows dental professionals to devote more time to direct patient care. However, the integration of AI raises ethical concerns regarding data privacy and the implications of algorithmic decision-making, underscoring the necessity for human oversight to maintain ethical standards in patient care. In addition, we acknowledge limitations in the current literature, such as variability in study methodologies and the need for longitudinal studies to assess AI’s long-term efficacy. Ultimately, our findings illustrate AI’s transformative potential in dentistry, paving the way for a future characterized by more precise, efficient, and patient-centered care.

Orthodontics, the dental specialty dedicated to correcting misaligned teeth and enhancing dental aesthetics, has experienced notable progress with the incorporation of artificial intelligence (AI). This article offers an in-depth review of AI’s role in the field, emphasizing recent advancements, current uses, and potential future developments. By leveraging AI, orthodontics has seen significant improvements in diagnosis, treatment planning, and patient outcomes. The discussion also explores the promising directions AI technology may take in further revolutionizing orthodontic care. AI is being applied across multiple aspects of orthodontic practice, with notable applications in image analysis, treatment planning, and outcome prediction. AI-powered software can analyze dental images, including X-rays, panoramic radiographs, and intraoral scans, to detect and diagnose orthodontic conditions with high accuracy. Furthermore, AI algorithms can assist orthodontists in creating personalized treatment plans by simulating tooth movements, predicting treatment outcomes, and optimizing treatment sequences. These systems also support remote monitoring of patient progress, enhancing treatment adherence, and facilitating timely interventions. Looking ahead, the future of AI in orthodontics holds immense promise for further advancements and innovations. Continued research and development efforts are expected to refine existing AI algorithms and expand the scope of AI applications in orthodontic practice. Areas of future exploration include the integration of AI with emerging technologies, such as 3D printing and virtual reality, to enhance treatment customization and patient engagement. In conclusion, AI is poised to revolutionize orthodontic practice, offering unprecedented opportunities to enhance diagnosis, treatment planning, and patient care. By harnessing the transformative power of AI, the orthodontic community can advance the field, improve treatment outcomes, and ultimately enhance the quality of care for patients worldwide.

In dentistry, artificial intelligence (AI) has shown great potential in improving diagnosis, treatment planning, and patient care. AI algorithms have been developed to analyze dental images, such as radiographs and intraoral scans, aiding in the detection of dental caries, periodontal diseases, and oral lesions. These algorithms can provide more accurate and efficient diagnoses, reducing the reliance on human interpretation. AI has also been utilized in treatment planning and helping dentists to determine the optimal approach for procedures such as dental implant placement and orthodontic treatment, and recently, AI has also been playing a significant role in forensic medicine as well as in forensic odontology. In addition, AI-based chatbots and virtual assistants have been developed to provide patients with personalized oral health information and guidance. Despite the numerous advantages, challenges remain in implementing AI in dentistry, such as ensuring data privacy and addressing ethical concerns. Nevertheless, AI has the potential to revolutionize dentistry by improving diagnosis, treatment planning, and patient care, ultimately leading to better oral health outcomes. This review provides an overview of the current applications of AI and its influence on dental practice, along with future prospects.

The integration of artificial intelligence (AI) into dental care holds the promise of revolutionizing the field by enhancing the accuracy of dental diagnosis and treatment. This paper explores the impact of AI in dental care, with a focus on its applications in diagnosis, treatment planning, and patient engagement. AI-driven dental imaging and radiography, computer-aided detection and diagnosis of dental conditions, and early disease detection and prevention are discussed in detail. Moreover, the paper delves into how AI assists in personalized treatment planning and provides predictive analytics for dental care. Ethical and privacy considerations, including data security, fairness, and regulatory aspects, are addressed, highlighting the need for a responsible and transparent approach to AI implementation. Finally, the paper underscores the potential for a collaborative partnership between AI and dental professionals to offer the best possible care to patients, making dental care more efficient, patient-centric, and effective. The advent of AI in dentistry presents a remarkable opportunity to improve oral health outcomes, benefiting both patients and the healthcare community.