Revolutionizing Dental Diagnostics: Advancements And Challenges In Ai-Powered Imaging Systems

Abstract

This study explores AI-powered dental imaging systems in detail, emphasizing how they are revolutionizing diagnoses. These systems analyze radiographs, cone-beam computed tomography (CBCT) scans, and magnetic resonance imaging (MRI) scans with efficiency thanks to machine learning algorithms. We give a summary of their potential for automated in[1]terpretation, treatment suggestions, and dental disease prognosis. Reviews from academic institutions highlight how much better they are at identifying dental decay and creating individualized treatment plans than older techniques.

Furthermore, the effectiveness of computer vision methods—in particular, convolutional neural networks (CNNs)—in identifying dental caries is emphasized. Issues with interpretability, ethical issues, and workflow integration still exist despite their promise. In order to overcome these obstacles and optimize the advantages of AI in dentistry, we stress the significance of interdisciplinary cooperation, which will eventually improve patient care and results.