Artificial Intelligence in Diagnostic Dental Radiology: A Comprehensive Review

Abstract

Artificial intelligence has quietly revolutionized how we approach diagnostic dental radiology, bringing remarkable new capabilities for automatically spotting and categorizing oral diseases. This review takes stock of where AI currently stands in dental imaging, with particular attention to deep learning applications across bitewing X-rays, panoramic films, and cone-beam CT scans. We examine how convolutional neural networks are being deployed to catch cavities, evaluate gum disease, analyze root canal needs, and plan implant procedures. Recent research shows these AI systems can match dental specialists in accuracy for detecting cavities between teeth (achieving AUC scores above 0.90) and identifying infections at root tips, all while cutting interpretation time by as much as 60%. That said, real-world adoption faces significant headwinds: scarce labeled datasets, built-in algorithmic biases, regulatory red tape, and the persistent “black box” problem that makes deep learning decisions difficult to interpret. Drawing together evidence from publications between 2019 and 2024, this article offers clinicians and researchers a balanced look at what AI can and cannot do in dental radiology today and where it might be headed tomorrow.