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This narrative review surveys current and emerging AI uses in implant dentistry, emphasizing machine learning, neural networks, and computer vision. It discusses AI for digital implant planning, surgical navigation, peri-implant disease monitoring, risk assessment, and predicting peri-implantitis and implant failure, supporting more efficient workflows, personalized strategies, and improved cost-effectiveness, with future perspectives and educational implications addressed.",{"@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/ai-powered-predictive-models-in-implant-dentistry-planning-risk-assessment-and-outcomes/461539/",{"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/ai-powered-predictive-models-in-implant-dentistry-planning-risk-assessment-and-outcomes/461539.png","ImageObject",300,407,{"name":92,"@type":93},"awa","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-08","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does AI support digital planning and navigation in implant dentistry?","Question",{"text":112,"@type":113},"AI is used in digital implant planning and surgical navigation, enhancing the planning accuracy and helping guide procedures through more informed clinical decision-making.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What kinds of AI models and methods are emphasized in the review?",{"text":117,"@type":113},"The review focuses on machine learning, neural networks, and computer vision, including supervised learning approaches for diagnostic and prognostic modeling.",{"name":119,"@type":110,"acceptedAnswer":120},"Which clinical outcomes does the review describe as being predicted using AI?",{"text":121,"@type":113},"AI is discussed for predicting treatment outcomes such as peri-implantitis and implant failure, alongside peri-implant disease monitoring and risk assessment.","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},461539,1791026874,{"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":81,"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},3985747858093,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Review  \nAI-Powered Predictive Models in Implant Dentistry: Planning, Risk Assessment, and Outcomes  \nGhada Neji 1, Roberta Gasparro 2, Mohamed Tlili 1, Aya Dhahri 1, Faten Khanfir 1, Gilberto Sammartino 2, *, Angelo Aliberti 2, Maria Domenica Campana 2 and Faten Ben Amor 1  \nAcademic Editor: James Kit-HonTsoi  \nReceived: 12 November 2025  \nRevised: 21 December 2025  \nAccepted: 24 December 2025  \nPublished: 27 December 2025  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.  \n1 Oral and Facial Rehabilitation Research Laboratory, Faculty of Dentistry, University of Monastir,  \nMonastir 5000, Tunisia; [nejighadaa96@yahoo.fr](nejighadaa96@yahoo.fr) (G.N.); [drtlilimohamed@gmail.com](drtlilimohamed@gmail.com) (M.T.);  \naya.dhahri@yahoo.fr (A.D.); khanfirfaten@yahoo.fr (F.K.); faten.benamor@yahoo.fr (F.B.A.)  \n2 Department of Neuroscience, Reproductive Science and Dentistry, University of Naples Federico II,  \n80131 Naples, Italy; [roberta.gasparro@unina.it](roberta.gasparro@unina.it) (R.G.); [ange.aliberti@studenti.unina.it](ange.aliberti@studenti.unina.it) (A.A.); [mariadomenica.campana@gmail.com](mariadomenica.campana@gmail.com) (M.D.C.)  \n* Correspondence: [gilberto.sammartino@unina.it](gilberto.sammartino@unina.it)  \nAbstract  \nArtificial intelligence (AI) is rapidly transforming the landscape of dental implantology by enhancing every stage of treatment, from diagnostics and digital planning to intraoperative navigation, outcome prediction, and long-term follow-up. This narrative review explores the current and emerging applications of AI technologies in implant dentistry, with a focus on machine learning, neural networks, and computer vision. It examines how AI is utilized in digital implant planning, surgical navigation, peri-implant disease monitoring, risk assessment, and the prediction of treatment outcomes such as peri-implantitis and implant failure. These innovations contribute to more efficient workflows, more personalized treatment strategies, and improved cost-effectiveness of care. Finally, future perspectivesand educational implications of AI integration in clinical implantology are discussed.  \nKeywords: artificial intelligence; implant dentistry; implant planning; implant navigation  \n1. Introduction  \nOver recent years, digital dentistry has undergone significant advancements, leading to a profound transformation in diagnostic, planning, and therapeutic workflows. Innovations include low-dose, high-resolution cone-beam computed tomography (CBCT), intraoral scanners (IOS), computer-aided design and manufacturing (CAD/CAM) systems, three-dimensional (3D) medical printing technologies, and dynamic navigation systems [1] . While the digital workflow is applicable across various domains of dentistry and dentomaxillofacial practice, its most sophisticated applications are currently observed in the fields of implantology and restorative dentistry [2] . Among these technological developments, artificial intelligence (AI) has emerged as a transformative tool, increasingly integrated into multiple phases of dental treatment planning and execution. In implant dentistry, AI is facilitating a paradigm shift in clinical decision-making by enhancing the accuracy of planning, enabling more robust risk assessments, and improving the prediction of treatment outcomes [3,4] . Through the analysis of large-scale patient data, AI enables clinicians to make more accurate and individualized decisions, ultimately improving treatment success and reducing complications.  \nUnlike existing narrative and systematic reviews that primarily focus on cataloging artificial intelligence applications or reporting diagnostic accuracy, the present review  \nadopts a clinically oriented perspective by integrating diagnostic, prognostic, and surgical AI models within unified clinical decision-making ","cbCaijQZw7bBGlYB","https://ap.wps.com/l/cbCaijQZw7bBGlYB","pdf",253131,14,"English","# Abstract\n# Introduction\n# Literature Search\n# Overview of AI Learning Paradigms Relevant to Implant Dentistry","[{\"question\":\"How does AI support digital planning and navigation in implant dentistry?\",\"answer\":\"AI is used in digital implant planning and surgical navigation, enhancing the planning accuracy and helping guide procedures through more informed clinical decision-making.\"},{\"question\":\"What kinds of AI models and methods are emphasized in the review?\",\"answer\":\"The review focuses on machine learning, neural networks, and computer vision, including supervised learning approaches for diagnostic and prognostic modeling.\"},{\"question\":\"Which clinical outcomes does the review describe as being predicted using AI?\",\"answer\":\"AI is discussed for predicting treatment outcomes such as peri-implantitis and implant failure, alongside peri-implant disease monitoring and risk assessment.\"}]","AI-Powered Predictive Models in Implant Dentistry - Planning, Risk Assessment, and Outcomes | PDF",1790761834,35]