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Searches across Embase, Web of Science, PubMed, and Scopus included studies using machine learning for specific oral lesions with prospective or retrospective designs and reporting sensitivity and specificity. Pooled results show strong diagnostic performance with a high diagnostic odds ratio and excellent SROC performance, including high sensitivity, specificity, and negative predictive values.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/diagnostic-accuracy-of-artificial-intelligence-assisted-clinical-imaging-in-the-detection-of-oral-potentially-malignant-disorders-and-oral-cancer-a-systematic-review-and-meta-analysis/328548/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/diagnostic-accuracy-of-artificial-intelligence-assisted-clinical-imaging-in-the-detection-of-oral-potentially-malignant-disorders-and-oral-cancer-a-systematic-review-and-meta-analysis/328548.png","ImageObject",300,407,{"name":92,"@type":93},"Ezra","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main objective of this systematic review and meta-analysis?","Question",{"text":112,"@type":113},"To examine how AI algorithms are used to detect OPMD and oral cancerous lesions, and to assess variations in accuracy among different imaging tools used in these diagnostic processes.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which databases were searched for the included studies?",{"text":117,"@type":113},"The systematic search covered Embase, Web of Science, PubMed, and Scopus.",{"name":119,"@type":110,"acceptedAnswer":120},"What overall diagnostic performance did the AI-assisted approach show?",{"text":121,"@type":113},"AI-based screening showed a high diagnostic odds ratio and excellent diagnostic performance on SROC, with pooled sensitivity around 89.9% and specificity around 89.2%, along with a high negative predictive value.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},328548,1790141874,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},1099514068035,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","’Systematic Review and/or Meta-analysis  \nDiagnostic accuracy of artificial intelligence assisted clinical imaging in the detection of oral potentially malignant disorders and oral cancer: a systematic review and meta-analysis  \nJingWen Li, PhDa , Wai Ying Kot, BDSb , Colman Patrick McGrath, PhDc , Bik Wan Amy Chan, MB, ChBe , Joshua Wing Kei Ho, PhDd,f, Li Wu Zheng, PhDa,*  \nBackground: The objective of this study is to examine the application of artificial intelligence (AI) algorithms in detecting oral potentially malignant disorders (OPMD) and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes.  \nMaterials and methods: A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD. Sensitivity and specificity analyses were also required. Forest plots were generated to display overall diagnostic odds ratio (DOR), sensitivity, specificity, negative predictive values, and summary receiver operating characteristic (SROC) curves. Meta-regression analysis was conducted to examine potential differences among different imaging tools.  \nResults: The overall DOR for AI-based screening of OPMD and oral mucosal cancerous lesions from normal mucosa was 68 .438 (95% CI = [39 .484–118.623], I2 = 86%) . The area under the SROC curve was 0 .938, indicating excellent diagnostic performance. AI-assisted screening showed a sensitivity of 89 .9%(95% CI = [0 .866–0.925]; I2 = 81%), specificity of 89 .2%(95% CI = [0 .851–0.922], I2 = 79%), and a high negative predictive value of 89 .5%(95% CI = [0 .851–0.927], I2 = 96%) . Meta-regression analysis revealed no significant difference among the three image tools. After generating a GOSH plot, the DOR was calculated to be 49 .30, and the area under the SROC curve was 0 .877. Additionally, sensitivity, specificity, and negative predictive value were 90 .5%(95% CI [0 .873–0.929], I2 = 4%), 87.0%(95% CI [0 .813–0.912], I2 = 49%) and 90 . 1%(95% CI [0 .860–0.931], I2 = 57%), respectively. Subgroup analysis showed that clinical photography had the highest diagnostic accuracy.  \nConclusions: AI-based detection using clinical photography shows a high DOR and is easily accessible in the current era with billions of phone subscribers globally. This indicates that there is significant potential for AI to enhance the diagnostic capabilities of general practitioners to the level of specialists by utilizing clinical photographs, without the need for expensive specialized imaging equipment. Keywords: artificial intelligence, diagnostic test, oral cancer, oral potentially malignant disorders  \nIntroduction  \nOral cancer is a common malignancy worldwide, with over 377 000 cases diagnosed and 177 000 deaths per year[1] . According to Surveillance, Epidemiology, and End Results  \n(SEER) Program data, the 5-year survival rate of oral cancer decreases as the disease progresses, from 86.3% in the localized stage to 39 .3% in the distant stage[2] . The diagnosis of oral cancer is sometimes preceded by oral potentially malignant disorders (OPMD), which is a significant group of disorders that affect the  \naDivision of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, bFaculty of Dentistry, The University of Hong Kong, cDivision of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, dSchool of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, eDepartment of Anatomical and Cellular Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong and fLaboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, People’s Republic of China  \nSponsorships or competing interests that m","cbCailm6R5sYL4Sx","https://ap.wps.com/l/cbCailm6R5sYL4Sx","pdf",2435503,13,"English","# Background\n## Materials and methods\n## Results\n## Conclusions\n# Introduction\n## Conventional screening method","[{\"question\":\"What is the main objective of this systematic review and meta-analysis?\",\"answer\":\"To examine how AI algorithms are used to detect OPMD and oral cancerous lesions, and to assess variations in accuracy among different imaging tools used in these diagnostic processes.\"},{\"question\":\"Which databases were searched for the included studies?\",\"answer\":\"The systematic search covered Embase, Web of Science, PubMed, and Scopus.\"},{\"question\":\"What overall diagnostic performance did the AI-assisted approach show?\",\"answer\":\"AI-based screening showed a high diagnostic odds ratio and excellent diagnostic performance on SROC, with pooled sensitivity around 89.9% and specificity around 89.2%, along with a high negative predictive value.\"}]","Diagnostic accuracy of artificial intelligence assisted clinical imaging in the detection of oral potentially malignant disorders and oral cancer: a systematic review and meta-analysis | PDF",1789980142,33]