{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T02:31:11Z","timestamp":1788229871911,"version":"build-2803163510"},"reference-count":23,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>Early diagnosis and carious lesion detection through artificial intelligence (AI) have transformed current standard methodologies because it generates precise results which work more efficiently and dependably. AI uses machine learning and deep learning technologies with computer-aided diagnostic systems to accomplish exceptional image evaluation of radiographic data and clinical records in dental caries detection through intraoral scans. This review discusses both prevailing challenges which limit and potential future uses of AI in dental diagnosis together with its ability to become a part of standard clinical work routines. Various researchers confirmed that AI works as a helpful tool which supports dental experts by improving diagnosis and minimizing human biases to enhance preventive care effects for patients.<\/jats:p>","DOI":"10.4103\/jcde.jcde_172_25","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T01:00:30Z","timestamp":1746493230000},"page":"401-405","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":20,"title":["Revolutionizing the diagnosis of dental caries using artificial intelligence-based methods"],"prefix":"10.4103","volume":"28","author":[{"given":"Jasmine","family":"Marwaha","sequence":"first","affiliation":[{"name":"Department of Conservative Dentistry and Endodontics, SGT Dental College, Hospital and Research Institute, SGT University, Gurugram, Haryana, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mamta","family":"Singla","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry and Endodontics, SGT Dental College, Hospital and Research Institute, SGT University, Gurugram, Haryana, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amar","family":"Nath","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences and Engineering, Sant Longowal Institute of Engineering and Technology, Sangrur, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashtha","family":"Arya","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry and Endodontics, SGT Dental College, Hospital and Research Institute, SGT University, Gurugram, Haryana, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2025,5,6]]},"reference":[{"key":"R1-20260423","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1177\/0022034515573272","article-title":"Global burden of untreated caries: A systematic review and metaregression","volume":"94","author":"Kassebaum","year":"2015","journal-title":"J Dent Res"},{"key":"R2-20260423","doi-asserted-by":"crossref","first-page":"103425.","DOI":"10.1016\/j.jdent.2020.103425","article-title":"Detecting caries lesions of different radiographic extension on bitewings using deep learning","volume":"100","author":"Cantu","year":"2020","journal-title":"J Dent"},{"key":"R3-20260423","first-page":"103615.","article-title":"Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study","volume":"107","author":"Mertens","year":"2021","journal-title":"J Dent"},{"key":"R4-20260423","doi-asserted-by":"crossref","first-page":"3840.","DOI":"10.1038\/s41598-019-40414-y","article-title":"A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films","volume":"9","author":"Chen","year":"2019","journal-title":"Sci Rep"},{"key":"R5-20260423","first-page":"105","article-title":"Deep learning for diagnosis of chronic periodontitis: A periodontal tissue instance segmentation network","volume":"194","author":"Zhang","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"R6-20260423","doi-asserted-by":"crossref","first-page":"17030.","DOI":"10.1038\/nrdp.2017.30","article-title":"Dental caries","volume":"3","author":"Pitts","year":"2017","journal-title":"Nat Rev Dis Primers"},{"key":"R7-20260423","doi-asserted-by":"crossref","first-page":"81","DOI":"10.3109\/00016357.2013.822548","article-title":"Caries risk assessment. 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