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The oral microbiome, a dynamic microbial community in the oral cavity, offers biomarker potential for predicting and tracking cancer development. This review synthesizes learning-based algorithms applied to oral microbiome data for cancer prediction, highlighting machine learning methods such as logistic regression, random forests, and artificial neural networks. A PubMed search over 10 years identified 3382 records and 44 included studies. Despite promising AI impact on microbiome–cancer understanding, clinical translation requires standardized protocols, curated diverse cohorts, and validation via large-scale, multi-centric, longitudinal research to improve early detection, risk stratification, and personalized OSCC management.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/recent-advancements-in-artificial-intelligence-powered-cancer-prediction-from-oral-microbiome/370086/",{"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/recent-advancements-in-artificial-intelligence-powered-cancer-prediction-from-oral-microbiome/370086.png","ImageObject",300,407,{"name":92,"@type":93},"anakgang17","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-24","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early detection of oral squamous cell carcinoma (OSCC) important?","Question",{"text":112,"@type":113},"OSCC is often diagnosed at late stages, and the overall survival improvement has been inadequate, especially for advanced patients. Early detection supports better outcomes and more timely interventions.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the oral microbiome support cancer prediction?",{"text":117,"@type":113},"The oral microbiome is an active microbial community in the oral cavity. Specific microbial patterns within this community can act as biomarkers for predicting cancer risk, progression, and recurrence.",{"name":119,"@type":110,"acceptedAnswer":120},"Which AI and machine learning approaches are discussed for oral microbiome-based prediction?",{"text":121,"@type":113},"The review describes learning-based algorithms and typical machine learning methods such as logistic regression, random forests, and artificial neural networks that learn microbial patterns linked to oral cancer.","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},370086,1790813663,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"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},962090883568,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","DOI: 10. 1111/prd.70000  \nR E V I E W A RTI C LE  \nRecent advancements in artificial intelligence-powered cancer prediction from oral microbiome  \nNegin Soghli1 | Aminollah Khormali2 | Darius Mahboubi2 | Aimin Peng1,3  |  \nPatricia A. Miguez3,4   \n1Department of Biomedical Sciences, Adams School of Dentistry, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA  \n2Adams School of Dentistry, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 3 Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA  \n4Department of Periodontology, Endodontics and Dental Hygiene, Adams School of Dentistry, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA  \nCorrespondence  \nPatricia A. Miguez, Department of Periodontology, Endodontics and Dental Hygiene, Adams School of Dentistry, The University of North Carolina at Chapel Hill, CB\\# 7455, Koury Oral Health Sciences, 385 S. Columbia St. , Chapel Hill, NC 27599-7455, USA. [Email:](Email: miguezp@unc.edu)[ miguezp@unc.edu](Email: miguezp@unc.edu)  \nAimin Peng, Department of Biomedical Sciences, Adams School of Dentistry, The University of North Carolina at Chapel Hill, Rm. 3510 Koury Oral Health Sciences Building, 385 S. Columbia St. , Chapel Hill, NC 27599-7455, USA.  \nEmail: [penga@unc.edu](penga@unc.edu)  \nFunding information  \nNational Institutes of Health, Grant/Award Number: CA233037 and DE030427  \nAbstract  \nOral cancer is a major global health burden, ranking sixth in prevalence, with oralsquamous cell carcinoma (OSCC) being the most common type. Importantly, OSCC is often diagnosed at late stages, underscoring the need for innovative methods for early detection. The oral microbiome, an active microbial community within the oral cavity, holds promise as a biomarker for the prediction and progression of cancer. Emerging computational techniques in the artificial intelligence (AI) field have enabled the analysis of complex microbiome data sets to unravel the association between oral microbiome composition and oral cancer. This review provides a comprehensive overview of learning-based algorithms applied to oral microbiome data for cancer prediction. In particular, this work discusses how typical machine learning (ML) algorithms, such as logistic regression, random forests, and artificial neural networks, identify the unique microbial patterns associated with oral cancer and other malignancies. A search was conducted in Pubmed covering a 10-year period. The goal was to identify previous studies focused on the role of the oral microbiome in oral cancer prediction using AI-powered tools. The search strategy identified 3382 records in total, of which 44 studies met the inclusion criteria. While AI has shown a transformative power in understanding and revealing the oral microbiome's role in cancer studies, its application in clinical settings requires further efforts on standardization of protocols, curation of diverse cohorts, and validation through largescale multi-centric and longitudinal studies. The integration of AI with oral microbiome analysis holds significant promise for improving early detection, risk stratification, and personalized treatment strategies for OSCC. By identifying unique microbial patterns associated with cancer, AI-driven models offer a noninvasive, cost-effective tool to predict disease progression and guide clinical decision-making. However, translating these advancements into routine clinical practice requires standardized protocols, diverse patient cohorts, and validation through large-scale, longitudinal studies. Once implemented, this approach could transform oral cancer management, enabling timely interventions and improving patient outcomes.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided t","cbCaimuEAF4zK3oS","https://ap.wps.com/l/cbCaimuEAF4zK3oS","pdf",5179954,33,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"Why is early detection of oral squamous cell carcinoma (OSCC) important?\",\"answer\":\"OSCC is often diagnosed at late stages, and the overall survival improvement has been inadequate, especially for advanced patients. Early detection supports better outcomes and more timely interventions.\"},{\"question\":\"How does the oral microbiome support cancer prediction?\",\"answer\":\"The oral microbiome is an active microbial community in the oral cavity. Specific microbial patterns within this community can act as biomarkers for predicting cancer risk, progression, and recurrence.\"},{\"question\":\"Which AI and machine learning approaches are discussed for oral microbiome-based prediction?\",\"answer\":\"The review describes learning-based algorithms and typical machine learning methods such as logistic regression, random forests, and artificial neural networks that learn microbial patterns linked to oral cancer.\"}]","Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome | PDF",1790186629,83]