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This systematic review evaluates current artificial intelligence (AI) applications for early diagnosis and risk prediction, emphasizing diagnostic accuracy, methodological diversity, and clinical translatability. A search of PubMed, Embase, Cochrane Library, Web of Science, and Scopus identified 63 high-quality studies. Analyses considered input modalities and algorithm evolution. Findings show strong sensitivity and specificity and a trend toward multimodal fusion, lightweight models, and improved performance, while limitations include small samples, limited external validation, and low interpretability. Clinical translation requires large multicenter datasets, prospective validation, greater transparency, and attention to ethical and privacy concerns.",{"@graph":69,"@context":126},[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/artificial-intelligence-and-its-application-in-early-oral-cancer-screening-a-systematic-review/353975/",{"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/artificial-intelligence-and-its-application-in-early-oral-cancer-screening-a-systematic-review/353975.png","ImageObject",300,407,{"name":92,"@type":93},"Sophia Brooks","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-23","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of this systematic review on AI for oral cancer screening?","Question",{"text":112,"@type":113},"The review evaluates current AI technology use in early oral cancer diagnosis and risk prediction, focusing on diagnostic accuracy, methodological diversity, and clinical translatability.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were studies selected and analyzed in the review?",{"text":117,"@type":113},"A systematic search was conducted across PubMed, Embase, Cochrane Library, Web of Science, and Scopus, incorporating 63 high-quality studies. Analysis considered data input modalities and the evolution of AI algorithms, following standard systematic review protocols.",{"name":119,"@type":110,"acceptedAnswer":120},"What diagnostic performance did AI models show for early oral lesions?",{"text":121,"@type":113},"AI models demonstrated high sensitivity and specificity for detecting early oral lesions and differentiating precancerous lesions, with trends toward multimodal fusion and lightweight, high-performance development.",{"name":123,"@type":110,"acceptedAnswer":124},"What challenges limit clinical translation of AI models for early screening?",{"text":125,"@type":113},"Many studies faced insufficient sample sizes, limited external validation, and poor model interpretability. The review emphasizes building large multicenter datasets, conducting rigorous prospective validation, improving transparency, and addressing ethical and privacy concerns.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},353975,1790146147,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149},962084925636,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","TYPE Systematic Review PUBLISHED 20 March 2026  \nDOI 10.3389/fonc.2026.1789708  \nOPEN ACCESS  \nEDITED BY  \nMaria Antonella Laginestra,  \nRizzoli Orthopedic Institute (IRCCS), Italy  \nREVIEWED BY  \nSatheeskumar R,  \nNarasaraopeta Engineering College, India Shahd A. Alajaji,  \nKing Saud University, Saudi Arabia  \n*CORRESPONDENCE  \nWeibo Huang  \n [huangwb@whu.edu.cn](huangwb@whu.edu.cn)  \nRECEIVED 30 January 2026  \nREVISED 26 February 2026  \nACCEPTED 03 March 2026  \nPUBLISHED 20 March 2026  \nCITATION  \nHuang W (2026) Artificial intelligence and its application in early oral cancer screening: a systematic review.  \nFront. Oncol. 16:1789708 .  \ndoi: 10.3389/fonc.2026.1789708  \nCOPYRIGHT  \n© 2026 Huang. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nArtificial intelligence and its application in early oral cancer screening: a systematic review  \nWeibo Huang*  \nSchool & Hospital of Stomatology, Wuhan University, Wuhan, China  \nOral cancer is a globally prevalent and life-threatening malignancy, where early detection can significantly improve prognosis and reduce mortality. Traditional screening methods are often limited by operator dependence, invasiveness, and high costs, leading to frequent late diagnoses. This systematic review aims to evaluate the current application of artificial intelligence (AI) technology in the early diagnosis and risk prediction of oral cancer, with a focus on diagnostic accuracy, methodological diversity, and clinical translatability. Methods: We conducted a systematic search across five databases (PubMed, Embase, Cochrane Library, Web of Science, and Scopus), incorporating 63 high-quality studies. The analysis was performed at two levels: data input modalities and the evolution of AI algorithms. Study selection, data extraction, and quality assessment followed standard systematic review protocols. Results: AI models demonstrated high sensitivity and specificity in detecting early oral lesions and differentiating precancerous lesions, showing a trend toward multimodal fusion, lightweight, and highperformance development. However, most studies faced challenges such asinsufficient sample sizes, limited external validation, and poor model interpretability. Conclusion: AI holds significant potential for improving early oral cancer screening. To fully realize its clinical value, it is essential to establish large-scale multicenter datasets, conduct rigorous prospective validation, enhance model transparency, and address ethical and privacy concerns.  \nKEYWORDS  \nartificial intelligence, clinical photography, deep learning, early screening, medical imaging  \n1 Introduction  \nOral cancer represents a significant global public health challenge (1) . Annually, approximately 370,000 new cases are diagnosed and 170,000 deaths result from this disease, with over two-thirds of these cases occurring in Asia (2) . Established high-risk factors include tobacco use, alcohol consumption, betel nut chewing, and human papillomavirus (HPV) infection. Notably, the synergistic interaction between betel nut use and tobacco smoking substantially elevates the risk of oral cancer in South and Southeast Asian countries (3) . Oral cancer poses a life-threatening risk and significantly compromises patients’ speech, swallowing, and masticatory functions, imposing substantial long-term social and economic burdens (4, 5) .  \nDespite the disease’s high preventability, early detection rates remain suboptimal. Over 60% of patients receive a diagnosis at stages III–IV, at these advanced stages, the 5-year survival rate is typically below 50% . Conversely","cbCaiesZ6jTq4Ueu","https://ap.wps.com/l/cbCaiesZ6jTq4Ueu","pdf",2168364,18,"English","# Introduction\n## Global burden and risk factors of oral cancer\n## Limitations of traditional screening methods\n## Rationale for AI in early cancer screening\n# Methods\n## Systematic search strategy and included studies\n## Data input modalities and algorithm evolution\n## Study selection, data extraction, and quality assessment\n# Results\n## Diagnostic performance for early oral lesions\n## Trends in model development and multimodal approaches\n# Discussion and Limitations\n## Sample size, external validation, and interpretability challenges\n# Conclusion\n## Requirements for clinical translation","[{\"question\":\"What is the purpose of this systematic review on AI for oral cancer screening?\",\"answer\":\"The review evaluates current AI technology use in early oral cancer diagnosis and risk prediction, focusing on diagnostic accuracy, methodological diversity, and clinical translatability.\"},{\"question\":\"How were studies selected and analyzed in the review?\",\"answer\":\"A systematic search was conducted across PubMed, Embase, Cochrane Library, Web of Science, and Scopus, incorporating 63 high-quality studies. Analysis considered data input modalities and the evolution of AI algorithms, following standard systematic review protocols.\"},{\"question\":\"What diagnostic performance did AI models show for early oral lesions?\",\"answer\":\"AI models demonstrated high sensitivity and specificity for detecting early oral lesions and differentiating precancerous lesions, with trends toward multimodal fusion and lightweight, high-performance development.\"},{\"question\":\"What challenges limit clinical translation of AI models for early screening?\",\"answer\":\"Many studies faced insufficient sample sizes, limited external validation, and poor model interpretability. The review emphasizes building large multicenter datasets, conducting rigorous prospective validation, improving transparency, and addressing ethical and privacy concerns.\"}]","Artificial intelligence and its application in early oral cancer screening - a systematic review | PDF",1790108286,45]