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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">APM</journal-id><journal-title-group><journal-title>Advances in Precision Medicine</journal-title></journal-title-group><issn>2424-8592</issn><eissn>2424-9106</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/APM.v11i7.15571</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>AI-Assisted Oral Imaging Diagnosis in Clinical Applications of Dentistry: A Review</title><url>https://artdesignp.com/journal/APM/11/7/10.26689/APM.v11i7.15571</url><author>ZhengSiping</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>11</volume><issue>7</issue><history><date date-type="pub"><published-time>2026-07-26</published-time></date></history><abstract>Artificial intelligence (AI), particularly deep learning and convolutional neural networks (CNNs), is increasingly used in dental and maxillofacial imaging for classification, detection, segmentation, and quantitative assessment. Applications include dental caries, periodontal bone loss, jaw lesions, orthodontic measurements, and impacted-tooth localization. This narrative review emphasizes the technical foundations of AI-assisted oral imaging, including CNN architectures, preprocessing, annotation, augmentation, evaluation, and external validation, while summarizing representative clinical applications. Current studies report promising performance in selected tasks, but generalizability is constrained by heterogeneous datasets, imaging protocols, annotation standards, and limited multicenter validation. Clinical translation further requires explainability, workflow integration, regulatory oversight, and prospective evaluation.</abstract><keywords>Artificial intelligence,Oral imaging,Diagnosis,Deep learning,Dental caries,Periodontal disease,CBCT</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Miki Y, Muramatsu C, Hayashi T, et al., 2017, Classification of Teeth in Cone-Beam CT Using Deep Convolutional Neural Network. Computers in Biology and Medicine, 80: 24&amp;ndash;29.
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