Application of deep machine learning in dental education: a systematic review of effectiveness in dental students' teaching learning outcomes.

Artificial Intelligence in Dental Education: Overview of Teaching, Assessment and Academic Performance Prediction

Artificial Intelligence (AI) is increasingly used in dental education. The present study aimed to examine the current landscape of AI applications in dental education, focusing on their impact on teaching, assessment, and the prediction of academic performance. A literature search was performed based on articles published between November 2014 and March 2026 in the PubMed and Scopus databases. Studies focusing on AI applications in dental education were analysed across the domains: teaching and learning, assessment, and academic performance prediction. Forty-two publications met the inclusion criteria and were used as the evidential basis for this study. AI integration emphasised fundamental knowledge, use cases, and evaluation in teaching and learning. AI tools such as chatbots, simulations, and generative models enhanced student engagement and learning efficiency while supporting clinical decision-making. AI-assisted tasks, including radiograph interpretation and scientific writing, demonstrated improved outcomes. For assessment, AI showed moderate to high correlations with human evaluators for essay grading and thematic analysis. Further, AI models could forecast student outcomes, supporting personalised learning strategies and early intervention. AI applications have a vast potential to enhance dental education, particularly in improving teaching efficiency, personalised learning, self-directed learning, and assessment accuracy. Curriculum revisions and faculty training are necessary to fully integrate AI responsibly, leveraging its capabilities while maintaining the critical role of human oversight in education.

Artificial intelligence for radiographic caries detection in dental education: a systematic review with functional meta-synthesis.

Machine Learning and Artificial Intelligence Models Applied to the Detection and Classification of Dental Caries: A Systematic Literature Review

Objective: To systematically evaluate the performance of artificial intelligence (AI) and machine learning (ML) models for the detection and classification of dental caries across different imaging modalities. Review methodology: A systematic literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar covering publications from January 2015 through March 2026. Studies reporting quantitative performance metrics for AI/ML models applied to caries detection, classification, or segmentation were included. A three-stage screening process (title, abstract, full-text) was applied following PRISMA guidelines. Results: A total of 45 studies were included. Deep learning, particularly convolutional neural networks, constituted the predominant approach (~69%). The most employed architectures include the YOLO family (v3–v11), U-Net variants, and transfer-learning classifiers (ResNet, VGG, DenseNet, EfficientNet, MobileNet). Reported accuracy ranged from 73.3% to 98.6%, with pooled meta-analytic sensitivity of 0.85 and specificity of 0.90. Only 12% of studies used public datasets. Conclusions: AI/ML systems demonstrate substantial diagnostic potential for dental caries, frequently matching clinician performance. Critical gaps persist regarding multicenter validation, clinically interpretable models, and prospective trials evaluating patient outcomes.

Artificial intelligence and technology in paediatric dentistry: a review

The field of pediatric dentistry is undergoing a paradigm shift, moving from a reactive model to one that embraces precision, prevention and the power of Artificial Intelligence (AI) and Digital Technologies (DT). These innovations offer exciting opportunities to transform diagnosis, management and treatment of paediatric oral health conditions. This study evaluates the role of AI and DT in pediatric dentistry, with emphasis on diagnostics, behavioral management, prevention and orthodontic planning. A thorough evaluation of the literature was carried out using Google Scholar, PubMed, Scopus and the Cochrane Library. A comprehensive review was conducted of peer-reviewed publications, clinical trials and meta-analyses published between 2015 and 2026 and included 38 studies. Four areas of evidence were combined: computer-aided orthodontic planning, digital behavioral therapies, smart preventative tracking and automated diagnostics. Deep learning (DL) models, including convolutional neural network (CNN), you only look once version 8 (YOLOv8) and inception residual network version 2 (Inception-ResNet-v2), demonstrated specialist-level accuracy in early caries detection, pathology mapping and cervical vertebral maturation staging. Virtual reality (VR) reduced procedural anxiety, while social robotics improved patient cooperation. Internet of Things (IoT)-enabled smart toothbrushes and machine learning models (ML) supported remote monitoring and personalized prevention; whereas intraoral scanning combined with three-dimensional (3D) printing enabled rapid fabrication of high-precision pediatric dental appliances. AI and DT are transforming pediatric dentistry by improving diagnostic accuracy, patient engagement, preventive care and treatment efficiency. However, their wider adoption requires robust datasets, standardized validation and multicenter clinical studies to ensure safe and effective implementation.

Teaching Methodologies and Educational Outcomes of Point‐of‐Care Ultrasound in Undergraduate Medical Education: A Scoping Review

ABSTRACT Objective This study aimed to identify the teaching methodologies used in point‐of‐care ultrasound (POCUS) education for undergraduate medical students and to synthesise the educational outcomes associated with these approaches. Methods A scoping review followed Joanna Briggs Institute methodology and Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐ScR) guidelines. The research question was developed using the Population–Concept–Context framework. Systematic searches were performed in PubMed/MEDLINE, Web of Science, Scopus, Embase, CINAHL and ERIC. The review protocol was prospectively registered in the Open Science Framework. Results Fifty‐nine studies were included. Teaching methodologies comprised supervised practice with real patients, simulation using high‐fidelity and low‐cost models, didactic lectures, active learning strategies (including flipped classroom and peer‐assisted learning), short and longitudinal courses and educational technologies such as e‐learning platforms, portable ultrasound devices and virtual reality. Assessment strategies included objective structured clinical examinations, structured questionnaires and theoretical and practical tests. The most frequently reported educational outcomes were improved diagnostic accuracy and clinical performance, technology‐supported theoretical learning, increased confidence and practical autonomy and the educational benefits of integrated active learning approaches. Educational interventions combining supervised practice, simulation and active learning were consistently associated with more favourable educational outcomes than isolated teaching strategies. Conclusions The structured and longitudinal integration of POCUS into undergraduate medical curricula appears to be a promising strategy for strengthening clinical reasoning, diagnostic accuracy and bedside decision‐making. These findings support implementing competency‐based curricula that integrate supervised practice, simulation and active learning methodologies, while highlighting the importance of longitudinal curricular integration for sustainable competency development.

AI-driven Dentistry

Background Artificial intelligence (AI) is increasingly being applied in healthcare and dentistry. Advances in machine learning, deep learning, natural language processing, reinforcement learning, and generative AI have introduced new approaches to dental-data analysis and clinical decision support. Objective This review summarizes current applications of AI across dental specialties and evaluates their potential contributions to diagnosis, treatment planning, risk prediction, and individualized patient care. Materials and Methods Published evidence concerning AI applications in oral and maxillofacial radiology, orthodontics, prosthodontics, periodontology, endodontics, pediatric dentistry, oral pathology, restorative dentistry, implantology, oral surgery, and dental public health was narratively reviewed. Particular attention was given to the AI models used, their clinical applications, reported performance, and barriers to clinical translation. Results AI-based approaches have been extensively investigated for interpreting dental images and detecting caries, periapical lesions, oral pathology, and alveolar bone loss. Convolutional neural networks and transformer-based models have shown promising performance in radiographic analysis. Hybrid and ensemble models can combine radiographic, clinical, behavioral, and biological information for risk assessment. AI has also been used to simulate orthodontic tooth movement and craniofacial development, support digital prosthesis design and esthetic planning, predict periodontal disease progression, estimate dental age, assess pediatric behavioral patterns, and predict early childhood caries. However, many systems rely on small or nondiverse datasets, and independent external and prospective clinical validation remains limited. Conclusion AI has the potential to improve diagnostic consistency, treatment planning, risk prediction, and personalized dental care. Future research should prioritize transparent and interpretable models, diverse multicenter datasets, prospective clinical validation, privacy-preserving collaborative learning, and integration with existing clinical workflows. AI should augment rather than replace clinicians’ professional judgment.