[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127347-en":3,"doc-seo-127347-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127347,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",7,"Healthcare","Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation - Review","Artificial intelligence (AI) increasingly serves as a transformative tool in cardiology, especially for diagnosing and managing atrial fibrillation (AF), the most prevalent cardiac arrhythmia. This review critically assesses and synthesizes current AI methodologies and their clinical relevance across AF diagnosis, risk prediction, and therapeutic guidance. It evaluates advances in machine learning, deep learning, and natural language processing for AF detection, risk stratification, and decision-making, while highlighting constraints such as data privacy, explainability, and workflow integration.","University of Birmingham  \nArtificial Intelligence in the Diagnosis and Management of Atrial Fibrillation  \nȚica, Otilia; Champsi, Asgher; Duan, Jinming; Țica, Ovidiu  \nDOI:  \n10.3390/diagnostics15202561  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nȚica, O, Champsi, A, Duan, J & Țica, O 2025, 'Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation', Diagnostics, vol. 15, no. 20, 2561. [https://doi.org/10.3390/diagnostics15202561](https://doi.org/10.3390/diagnostics15202561)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 04. Aug. 2026  \nReview  \nArtificial Intelligence in the Diagnosis and Management of Atrial Fibrillation  \nOtilia T, ica 1, *,†, Asgher Champsi 2, Jinming Duan 3,4,† and Ovidiu T, ica 5,6, *,†  \nAcademic Editors: Rajesh K. Tripathy, Prince Jain, Haipeng Liu and Shonak Bansal  \nReceived: 31 July 2025  \nRevised: 7 October 2025  \nAccepted: 9 October 2025  \nPublished: 11 October 2025  \nCitation: T, ica, O.; Champsi, A.; Duan, J.; T, ica, O. Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation. Diagnostics 2025, 15, 2561. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diagnostics15202561  \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 ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Cardiology Clinic, Emergency County Clinical Hospital of Bihor, 410165 Oradea, Romania  \n2 Cardiovascular Sciences, College of Medicine and Health, University of Birmingham, Birmingham B15 2TT, UK; [a.champsi@bham.ac.uk](a.champsi@bham.ac.uk)  \n3 Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester M13 9PL, UK; [j.duan@bham.ac.uk](j.duan@bham.ac.uk)  \n4 School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK  \n5 Department of Morphological Disciplines, Faculty of Medicine and Pharmacy, University of Oradea,  \n410073 Oradea, Romania  \n6 Pathology Department, Emergency County Clinical Hospital of Bihor, 410165 Oradea, Romania  \n* Correspondence: [otilia_cristea01@yahoo.com](otilia_cristea01@yahoo.com) (O.T, .); [tica.ovidiu@didactic.uoradea.ro](tica.ovidiu@didactic.uoradea.ro) (O.T, .)† These auth","cbCaidKJJV2DIpMI","https://ap.wps.com/l/cbCaidKJJV2DIpMI","pdf",1413853,1,21,"English","en",105,"# Abstract\n## AI methods for AF detection\n## Risk prediction and therapeutic guidance\n## ECG analysis with deep learning\n## Adoption challenges and future directions","[{\"question\":\"AI在房颤诊断与管理中主要用于哪些方面？\",\"answer\":\"AI主要用于房颤检测、风险分层（风险预测）以及治疗决策支持，包括对抗凝治疗、节律控制与频率控制策略的个体化优化。\"},{\"question\":\"文中如何评价AI相较传统临床方法的优势？\",\"answer\":\"文中指出AI驱动工具在解读心电图（ECGs）、可穿戴持续监测，以及预测房颤发生与进展方面，较传统方法表现出更高的准确性与效率。\"},{\"question\":\"AI进入临床应用面临哪些限制？\",\"answer\":\"临床落地受到数据隐私、可解释性以及与临床工作流整合等挑战影响，文中建议通过更可靠的验证研究、透明算法开发与跨学科协作来应对。\"}]","Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation - Review | PDF",1785938415,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"artificial-intelligence-in-the-diagnosis-and-management-of-atrial-fibrillation-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/artificial-intelligence-in-the-diagnosis-and-management-of-atrial-fibrillation-review/127347/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"AI在房颤诊断与管理中主要用于哪些方面？","Question",{"text":76,"@type":77},"AI主要用于房颤检测、风险分层（风险预测）以及治疗决策支持，包括对抗凝治疗、节律控制与频率控制策略的个体化优化。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"文中如何评价AI相较传统临床方法的优势？",{"text":81,"@type":77},"文中指出AI驱动工具在解读心电图（ECGs）、可穿戴持续监测，以及预测房颤发生与进展方面，较传统方法表现出更高的准确性与效率。",{"name":83,"@type":74,"acceptedAnswer":84},"AI进入临床应用面临哪些限制？",{"text":85,"@type":77},"临床落地受到数据隐私、可解释性以及与临床工作流整合等挑战影响，文中建议通过更可靠的验证研究、透明算法开发与跨学科协作来应对。","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]