Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations
Atrial fibrillation (AF) is the most prevalent sustained arrhythmia and a major contributor to stroke and heart failure. Despite progress in management, challenges persist in early detection, risk stratification, and personalised treatment. Artificial intelligence (AI), especially machine learning (...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1596574/full |
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| author | Ibrahim Antoun Ibrahim Antoun Ahmed Abdelrazik Mahmoud Eldesouky Xin Li Georgia R. Layton Georgia R. Layton Georgia R. Layton Mustafa Zakkar Mustafa Zakkar Mustafa Zakkar Riyaz Somani Riyaz Somani G. André Ng G. André Ng G. André Ng |
| author_facet | Ibrahim Antoun Ibrahim Antoun Ahmed Abdelrazik Mahmoud Eldesouky Xin Li Georgia R. Layton Georgia R. Layton Georgia R. Layton Mustafa Zakkar Mustafa Zakkar Mustafa Zakkar Riyaz Somani Riyaz Somani G. André Ng G. André Ng G. André Ng |
| author_sort | Ibrahim Antoun |
| collection | DOAJ |
| description | Atrial fibrillation (AF) is the most prevalent sustained arrhythmia and a major contributor to stroke and heart failure. Despite progress in management, challenges persist in early detection, risk stratification, and personalised treatment. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has emerged as a transformative tool in AF care. This scoping review examines the applications of AI across key domains: detection, risk prediction, treatment optimisation, and remote monitoring. AI-driven models enhance AF detection by analysing ECGs and wearable device data with high accuracy, enabling early identification of asymptomatic cases. By incorporating diverse clinical, imaging, and genomic data, predictive models outperform conventional risk scores in estimating stroke risk and disease progression. In treatment, AI assists in personalised anticoagulation decisions, catheter ablation planning, and optimising antiarrhythmic drug selection. Furthermore, AI-powered remote monitoring integrates wearable-derived insights with real-time decision support, improving patient engagement and adherence. Despite these advances, significant challenges persist, including algorithm transparency, bias, data integration, and regulatory hurdles. Explainable AI (XAI) is crucial to ensure clinician trust and facilitate implementation into clinical workflows. Future research should focus on large-scale validation, multi-modal data integration, and real-world AI deployment in AF management. AI has the potential to revolutionise AF care, shifting from reactive treatment to proactive, personalised management. Addressing current limitations through interdisciplinary collaboration will be key to realising AI's full potential in clinical practice and improving patient outcomes. |
| format | Article |
| id | doaj-art-48e4b9d1d6854e8399e3a0fda7ea5b70 |
| institution | DOAJ |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-48e4b9d1d6854e8399e3a0fda7ea5b702025-08-20T03:16:16ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-06-011210.3389/fcvm.2025.15965741596574Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerationsIbrahim Antoun0Ibrahim Antoun1Ahmed Abdelrazik2Mahmoud Eldesouky3Xin Li4Georgia R. Layton5Georgia R. Layton6Georgia R. Layton7Mustafa Zakkar8Mustafa Zakkar9Mustafa Zakkar10Riyaz Somani11Riyaz Somani12G. André Ng13G. André Ng14G. André Ng15Department of Cardiology, Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, United KingdomDepartment of Cardiovascular Sciences, Clinical Science Wing, Glenfield Hospital, University of Leicester, Leicester, United KingdomDepartment of Cardiology, Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, United KingdomDepartment of Cardiology, Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, United KingdomDepartment of Cardiovascular Sciences, Clinical Science Wing, Glenfield Hospital, University of Leicester, Leicester, United KingdomDepartment of Cardiovascular Sciences, Clinical Science Wing, Glenfield Hospital, University of Leicester, Leicester, United KingdomDepartment of Cardiac Surgery, Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, United KingdomDepartment of Research, National Institute for Health Research Leicester Research Biomedical Centre, Leicester, United KingdomDepartment of Cardiovascular Sciences, Clinical Science Wing, Glenfield Hospital, University of Leicester, Leicester, United KingdomDepartment of Research, National Institute for Health Research Leicester Research Biomedical Centre, Leicester, United KingdomLeicester British Heart Foundation Centre of Research Excellence, Glenfield Hospital, Leicester, United KingdomDepartment of Cardiology, Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, United KingdomDepartment of Cardiovascular Sciences, Clinical Science Wing, Glenfield Hospital, University of Leicester, Leicester, United KingdomDepartment of Cardiology, Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, United KingdomDepartment of Cardiovascular Sciences, Clinical Science Wing, Glenfield Hospital, University of Leicester, Leicester, United KingdomLeicester British Heart Foundation Centre of Research Excellence, Glenfield Hospital, Leicester, United KingdomAtrial fibrillation (AF) is the most prevalent sustained arrhythmia and a major contributor to stroke and heart failure. Despite progress in management, challenges persist in early detection, risk stratification, and personalised treatment. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has emerged as a transformative tool in AF care. This scoping review examines the applications of AI across key domains: detection, risk prediction, treatment optimisation, and remote monitoring. AI-driven models enhance AF detection by analysing ECGs and wearable device data with high accuracy, enabling early identification of asymptomatic cases. By incorporating diverse clinical, imaging, and genomic data, predictive models outperform conventional risk scores in estimating stroke risk and disease progression. In treatment, AI assists in personalised anticoagulation decisions, catheter ablation planning, and optimising antiarrhythmic drug selection. Furthermore, AI-powered remote monitoring integrates wearable-derived insights with real-time decision support, improving patient engagement and adherence. Despite these advances, significant challenges persist, including algorithm transparency, bias, data integration, and regulatory hurdles. Explainable AI (XAI) is crucial to ensure clinician trust and facilitate implementation into clinical workflows. Future research should focus on large-scale validation, multi-modal data integration, and real-world AI deployment in AF management. AI has the potential to revolutionise AF care, shifting from reactive treatment to proactive, personalised management. Addressing current limitations through interdisciplinary collaboration will be key to realising AI's full potential in clinical practice and improving patient outcomes.https://www.frontiersin.org/articles/10.3389/fcvm.2025.1596574/fullatrial fibrillationartificial intelligencemachine learningECGrisk stratificationremote monitoring |
| spellingShingle | Ibrahim Antoun Ibrahim Antoun Ahmed Abdelrazik Mahmoud Eldesouky Xin Li Georgia R. Layton Georgia R. Layton Georgia R. Layton Mustafa Zakkar Mustafa Zakkar Mustafa Zakkar Riyaz Somani Riyaz Somani G. André Ng G. André Ng G. André Ng Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations Frontiers in Cardiovascular Medicine atrial fibrillation artificial intelligence machine learning ECG risk stratification remote monitoring |
| title | Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations |
| title_full | Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations |
| title_fullStr | Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations |
| title_full_unstemmed | Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations |
| title_short | Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations |
| title_sort | artificial intelligence in atrial fibrillation emerging applications research directions and ethical considerations |
| topic | atrial fibrillation artificial intelligence machine learning ECG risk stratification remote monitoring |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1596574/full |
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