Radiomics has recently begun as a transformative approach in medical imaging, shifting radiology from qualitative description to quantitative analysis. By extracting high-throughput features from CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET/CT (Positron Emission Tomography/Computed Tomography), and CBCT (Cone Beam Computed Tomography), radiomics enables the characterization of tissue heterogeneity and the development of imaging biomarkers with diagnostic, prognostic, and predictive values. This narrative review explores the historical evolution of radiomics and its methodological foundations, including acquisition, segmentation, feature extraction and modeling, and platforms supporting these workflows. Clinical applications are highlighted in oncology, cardiology, neurology, and musculoskeletal and dentomaxillofacial imaging. Despite being promising, radiomics faces challenges related to standardization, reproducibility, PACS/RIS (Picture Archiving and Communication System/Radiology Information System) integration and interpretability. Professional initiatives, such as the Image Biomarker Standardization Initiative (IBSI) and guidelines from radiological societies, are addressing these barriers by promoting harmonization and clinical translation. The ultimate vision is a radiomics-augmented radiology report in which validated biomarkers and predictive signatures complement conventional findings, thus enhancing objectivity, reproducibility, and advancing precision medicine.

This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as ‘presence of caries’ and 13,928 surfaces are determined as ‘absence of caries’ for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images.

Artificial intelligence (AI) is transforming dentomaxillofacial radiology education by enabling adaptive, personalized, and data-driven learning experiences. This review critically examines the pedagogical potential of AI within dental curricula, focusing on its ability to enhance student engagement, improve diagnostic competencies, and streamline clinical decision-making processes. Key innovations include real-time feedback systems, AI-guided simulations, automated assessments, and clinical decision support tools. Through these resources, AI transforms static learning into dynamic, interactive, and competency-based education. Additionally, this review discusses the integration of AI into formative assessment frameworks, such as OSCEs and mini-CEX, and its impact on student confidence, performance tracking, and educational scalability. Although primarily narrative in structure, this review synthesizes the current literature on dentomaxillofacial radiology education, supported by selected insights from medical radiology, to provide a comprehensive and up-to-date perspective on the educational applications of AI. Challenges (including ethical implications and other practical considerations) are addressed, alongside future directions for research and curriculum development. Overall, AI has the potential to significantly enhance radiology education by fostering clinically competent, ethically grounded, and technologically literate dental professionals.

Autops Case Rep, vol.9, n2, e2018073, 2019