{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T16:44:22Z","timestamp":1787071462924,"version":"build-2736575974"},"reference-count":10,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"Suppl 4","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Early detection and accurate classification of oral lesions are essential for effective clinical management and prevention of malignant transformation. Recent advancements in artificial intelligence (AI) have led to the development of diagnostic tools aimed at enhancing clinical decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>A prospective clinical study was conducted involving 180 patients presenting with suspicious oral lesions at a tertiary dental care center. Digital images of lesions were captured and analyzed using an AI-powered diagnostic software trained on a dataset of over 10,000 labeled images. Each case was also evaluated by two oral pathologists, followed by histopathological confirmation. Sensitivity, specificity, accuracy, and Cohen\u2019s kappa coefficient were calculated to assess diagnostic concordance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      The AI system achieved an overall diagnostic accuracy of 91.1% (\u00b14.3), sensitivity of 93.6%, and specificity of 89.2%. The concordance between AI and histopathological diagnosis was substantial (\u03ba =0.83;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.001). The highest performance was observed in detecting leukoplakia (95.2% accuracy) and oral lichen planus (92.4%). Misclassification occurred primarily in cases of mild dysplasia and nonspecific ulcerations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The AI-based diagnostic tool demonstrated high diagnostic accuracy and substantial agreement with histopathological findings. It shows promise as a chairside adjunct to enhance clinical decision-making and early detection of potentially malignant oral lesions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jpbs.jpbs_1229_25","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T08:00:10Z","timestamp":1766044810000},"page":"S3138-S3140","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of Artificial Intelligence-Based Diagnostic Tools for Accurate Detection and Classification of Oral Lesions: A Clinical Study"],"prefix":"10.4103","volume":"17","author":[{"given":"Khushali","family":"Shah","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Pathology, K. M. Shah Dental College and Hospital, Sumandeep Vidyapeeth Deemed to be University, Gujarat, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankit","family":"Dhimole","sequence":"additional","affiliation":[{"name":"Department of Oral Medicine and Radiology, Hitkarini Dental College and Hospital, Madhya Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neetu","family":"Pandey","sequence":"additional","affiliation":[{"name":"Department of Oral Medicine And Radiology, Mansarovar Dental College, Bhopal, Madhya Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robium","family":"Naorem","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics, Shija Academy of Health Sciences, Manipur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rangoli","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Department of Public Health Dentistry, Faculty of Dental Sciences, SGT University, Haryana, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sameer","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery, Inderprastha Dental College and Hospital, Sahibabad, Ghaziabad, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"R1-20251218","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.3390\/diagnostics12051029","article-title":"Artificial intelligence in the diagnosis of oral diseases: Applications and pitfalls","volume":"12","author":"Patil","year":"2022","journal-title":"Diagnostics (Basel)"},{"key":"R2-20251218","first-page":"e41227","article-title":"Smart smile: Revolutionizing dentistry with artificial intelligence","volume":"15","author":"Dhopte","year":"2023","journal-title":"Cureus"},{"key":"R3-20251218","doi-asserted-by":"crossref","first-page":"5032435","DOI":"10.1155\/2022\/5032435","article-title":"Uses of different machine learning algorithms for diagnosis of dental caries","volume":"2022","author":"Talpur","year":"2022","journal-title":"J Healthc Eng"},{"key":"R4-20251218","first-page":"e27405","article-title":"Artificial intelligence in dentistry: Past, present, and future","volume":"14","author":"Agrawal","year":"2022","journal-title":"Cureus"},{"key":"R5-20251218","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.12688\/f1000research.140204.1","article-title":"Contemporary role and applications of artificial intelligence in dentistry","volume":"12","author":"Bonny","year":"2023","journal-title":"F1000Res"},{"key":"R6-20251218","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.jds.2020.06.019","article-title":"Developments, application, and performance of artificial intelligence in dentistry-A systematic review","volume":"16","author":"Khanagar","year":"2021","journal-title":"J Dent Sci"},{"key":"R7-20251218","doi-asserted-by":"crossref","first-page":"7378","DOI":"10.3390\/jcm12237378","article-title":"Current progress and challenges of using artificial intelligence in clinical dentistry-A narrative review","volume":"12","author":"Surlari","year":"2023","journal-title":"J Clin Med"},{"key":"R8-20251218","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.1111\/iej.14127","article-title":"Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks","volume":"57","author":"Ourang","year":"2024","journal-title":"Int Endod J"},{"key":"R9-20251218","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1111\/jerd.13079","article-title":"Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review","volume":"35","author":"Tabatabaian","year":"2023","journal-title":"J Esthet Restor Dent"},{"key":"R10-20251218","first-page":"356","article-title":"Application of artificial intelligence in clinical dentistry, a comprehensive review of literature","volume":"24","author":"Ghods","year":"2023","journal-title":"J Dent (Shiraz)"}],"container-title":["Journal of Pharmacy and Bioallied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/jpbs.jpbs_1229_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:46:28Z","timestamp":1787067988000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/jpbs.jpbs_1229_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":10,"journal-issue":{"issue":"Suppl 4","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.4103\/jpbs.jpbs_1229_25","relation":{},"ISSN":["0976-4879","0975-7406"],"issn-type":[{"value":"0976-4879","type":"print"},{"value":"0975-7406","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"2025-07-13","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-10","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}