ARTIFICIAL INTELLIGENCE IN DIGITAL DENTISTRY IMAGING TO RISK PREDICTION

Abstract

Artificial Intelligence (Ai) Has Emerged As A Transformative Force In Digital Dentistry, offering Significant Advancements In Diagnostic Imaging, Risk Prediction, and Clinical Decision Support. This Review Examines The Integration of Deep Learning Architectures, Particularly Convolutional Neural Networks (Cnns), Into Dental Radiography, Cone-Beam Computed Tomography (Cbct), and Intraoral Scanning Workflows. Ai-Based Systems Demonstrate Diagnostic Accuracy Comparable To Trained Clinicians For Caries Detection, Periodontal Bone Loss Assessment, Periapical Pathology Identification, And Anatomical Landmark Detection. Methodological Advances Including Tripod-Ai, Probast-Ai, Prisma-Ai, Claim, and The Consensus-Based Checklist For Ai In Dentistry Frameworks Have Established Standards For Transparent Reporting and Rigorous Validation. However, Substantial Challenges Persist Regarding Data Heterogeneity, External Validation, Algorithmic Bias, and Clinical Workflow Integration. The Transition From Reactive Diagnosis To Prospective Risk Stratification Represents A Paradigm Shift Requiring Longitudinal Datasets, Calibration Analysis, and Federated Learning Approaches. Responsible Integration Of Ai Into Dental Practice Necessitates Addressing Interoperability, Clinician Literacy, Regulatory Compliance, and Ethical Considerations While Maintaining Human Oversight of Clinical Decision-Making. Future Directions Emphasize Harmonization of Reporting Standards, Multi-Site Validation, and Collaborative Frameworks Between Dental Professionals and Technologists To Ensure Ai Enhances Patient Care Without Compromising Professional Autonomy or Ethical Principles.

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