A Review on AI in Dental Radiography: A New Era of Diagnosis

Abstract

Artificial Intelligence (AI) is rapidly transforming dental radiology by enhancing diagnostic accuracy, workflow efficiency, and clinical decision-making. Utilizing machine learning, deep learning, convolutional neural networks (CNNs), and advanced image-processing algorithms, AI systems can automatically detect dental anomalies such as caries, periodontal disease, periapical lesions, cysts, tumors, and maxillofacial fractures from 2D and 3D radiographs. Applications extend to cephalometric analysis, bone age assessment, implant identification, forensic dentistry, and radiation dose optimization. AI-powered platforms also support education, training, and predictive analytics, enabling early intervention and personalized care. Despite these advances, challenges persist, including the need for large, diverse annotated datasets, standardization of reporting, ethical concerns, algorithmic bias, and the “black-box” nature of deep learning models. With continued collaboration among clinicians, radiologists, and AI developers, AI can serve as an augmentative tool that improves patient outcomes, streamlines workflows, and drives the evolution of precision dentistry.

Introduction

Artificial Intelligence (AI), introduced in 1956, simulates human cognitive functions and includes subfields such as machine learning, deep learning, natural language processing, and robotics. In dentistry, AI has emerged as a transformative tool, especially in oral and maxillofacial radiology, enhancing diagnostic accuracy, workflow efficiency, and treatment planning. AI systems can automatically detect a wide range of dental conditions—including caries, periapical lesions, periodontal bone loss, cysts, tumors, and lymph node metastasis—by analyzing 2D and 3D radiographs, digital scans, and CAD/CAM datasets.

Applications:

• Diagnostics: Early detection of dental anomalies, cephalometric analysis, orthodontic planning, and bone quality assessment for implants and osteoporosis.

• Workflow & Education: Automation reduces manual effort, improves patient throughput, and supports dental training through visualized X-ray analyses.

• Forensic Dentistry: Age and sex estimation, post-mortem identification, and reproducible objective results.

• Image Enhancement: Denoising low-dose scans and reducing metal artifacts.

Challenges:

• Need for large, diverse, annotated datasets for training AI models.

• Lack of standardized reporting formats.

• Ethical and legal concerns regarding data privacy, consent, and algorithmic bias.

• “Black-box” issue in deep learning models, prompting development of explainable AI techniques like CAM and Eigen-CAM.

• Cybersecurity risks, especially with digital imaging formats like DICOM.

Conclusion

The integration of artificial intelligence into dental radiography is steadily transforming diagnosis, patient care, and clinical efficiency by real-time diagnostic support and smooth integration with existing imaging systems. Beyond detection, AI’s predictive analytics enable early identification of potential dental problems, fostering proactive and evidence-based interventions, while its automated interpretation systems play a valuable role in training and educating new dentists by strengthening radiographic diagnostic skills. Importantly, AI is not positioned as a replacement for oral and maxillofacial radiologists, but rather as an augmentative tool that enhances their expertise, automates repetitive tasks, and provides reliable second opinions to improve accuracy and patient outcomes. The path forward lies in strong collaboration between radiologists, dentists, and AI specialists, ensuring that the technology is harnessed ethically, effectively, and innovatively to maximize its benefits and drive the future of dental care.

References

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Copyright

Copyright © 2025 Dr. Sreedevi J, Ebi Paradise M S, Elavarasu K, Fiona Vijay V, Gayathri Swetha G, Dr. Priya Ramani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.