Application of Artificial Intelligence in the Detection and Prediction of Atrial Fibrillation in ECG Recordings – A Narrative Review
DOI:
https://doi.org/10.12775/JEHS.2026.95.76576Keywords
Atrial Fibrillation; Artificial Intelligence; Deep Learning; Electrocardiography; Mass Screening; StrokeAbstract
Introduction and purpose. Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a leading cause of ischaemic stroke, yet it frequently remains undiagnosed because of its paroxysmal and often asymptomatic nature. Artificial intelligence (AI), and in particular deep learning applied to the electrocardiogram (ECG), has emerged as a transformative approach both for detecting AF when the arrhythmia is present and for predicting future AF from an ECG recorded during sinus rhythm. The purpose of this narrative review is to summarise the current state of knowledge on AI-enabled ECG analysis for the detection and prediction of AF and to critically appraise its clinical readiness.
Description of the state of knowledge. Convolutional neural networks can classify AF on single-lead and twelve-lead ECG with cardiologist-level accuracy (area under the curve, AUC, above 0.97 for rhythm classification) and can identify an "electrocardiographic signature" of AF on a normal sinus-rhythm tracing (AUC approximately 0.87). Consumer wearables using photoplethysmography and single-lead ECG achieve pooled sensitivity and specificity around 95%. Predictive models for incident AF reach AUC values of 0.78–0.85 and have been validated prospectively, enriching the diagnostic yield of screening. Nevertheless, false positives, unclassifiable tracings, limited external validity, and the absence of trials demonstrating a reduction in stroke temper enthusiasm.
Summary. AI-ECG is a mature and accurate tool for AF detection and a promising instrument for AF prediction, but its translation into improved patient-relevant outcomes remains unproven. Rigorous external validation, outcome-driven randomised trials, and clear regulatory and implementation frameworks are required before routine clinical adoption.
Keywords: Atrial Fibrillation; Artificial Intelligence; Deep Learning; Electrocardiography; Mass Screening; Stroke
References
1. Van Gelder IC, Rienstra M, Bunting KV, et al. 2024 ESC guidelines for the management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS). Eur Heart J. 2024;45(36):3314-3414. https://doi.org/10.1093/eurheartj/ehae176
2. Noseworthy PA, Kaufman ES, Chen LY, et al. Subclinical and device-detected atrial fibrillation: pondering the knowledge gap: a scientific statement from the American Heart Association. Circulation. 2019;140(25):e944-e963. https://doi.org/10.1161/CIR.0000000000000740
3. Kahwati LC, Asher GN, Kadro ZO, et al. Screening for atrial fibrillation: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2022;327(4):368-383. https://doi.org/10.1001/jama.2021.21811
4. Khera R, Oikonomou EK, Nadkarni GN, et al. Transforming cardiovascular care with artificial intelligence: from discovery to practice: JACC state-of-the-art review. J Am Coll Cardiol. 2024;84(1):97-114. https://doi.org/10.1016/j.jacc.2024.05.003
5. Elias P, Jain SS, Poterucha T, et al. Artificial intelligence for cardiovascular care—part 1: advances: JACC review topic of the week. J Am Coll Cardiol. 2024;83(24):2472-2486. https://doi.org/10.1016/j.jacc.2024.03.400
6. Guo RX, Tian X, Bazoukis G, et al. Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia. Pacing Clin Electrophysiol. 2024;47(6):789-801. https://doi.org/10.1111/pace.14995
7. Muzammil MA, Javid S, Afridi AK, et al. Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. J Electrocardiol. 2024;83:30-40. https://doi.org/10.1016/j.jelectrocard.2024.01.006
8. Armoundas AA, Narayan SM, Arnett DK, et al. Use of artificial intelligence in improving outcomes in heart disease: a scientific statement from the American Heart Association. Circulation. 2024;149(14):e1028-e1050. https://doi.org/10.1161/CIR.0000000000001201
9. Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021;18(7):465-478. https://doi.org/10.1038/s41569-020-00503-2
10. Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019;394(10201):861-867. https://doi.org/10.1016/S0140-6736(19)31721-0
11. Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25(1):65-69. https://doi.org/10.1038/s41591-018-0268-3
12. Kennedy A, Doggart P, Smith SW, et al. Device agnostic AI-based analysis of ambulatory ECG recordings. J Electrocardiol. 2022;74:154-161. https://doi.org/10.1016/j.jelectrocard.2022.09.004
13. Manetas-Stavrakakis N, Sotiropoulou IM, Paraskevas T, et al. Accuracy of artificial intelligence-based technologies for the diagnosis of atrial fibrillation: a systematic review and meta-analysis. J Clin Med. 2023;12(20):6576. https://doi.org/10.3390/jcm12206576
14. Hughes A, Shandhi MMH, Master H, Dunn J, Brittain E. Wearable devices in cardiovascular medicine. Circ Res. 2023;132(5):652-670. https://doi.org/10.1161/CIRCRESAHA.122.322389
15. Perez MV, Mahaffey KW, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med. 2019;381(20):1909-1917. https://doi.org/10.1056/NEJMoa1901183
16. Barrera N, Solorzano M, Jimenez Y, et al. Accuracy of smartwatches in the detection of atrial fibrillation: a systematic review and diagnostic meta-analysis. JACC Adv. 2025;4(11):102376. https://doi.org/10.1016/j.jacadv.2025.102376
17. Shahid S, Iqbal M, Saeed H, et al. Diagnostic accuracy of Apple Watch electrocardiogram for atrial fibrillation: a systematic review and meta-analysis. JACC Adv. 2025;4(2):101562. https://doi.org/10.1016/j.jacadv.2024.101562
18. Lubitz SA, Faranesh AZ, Selvaggi C, et al. Detection of atrial fibrillation in a large population using wearable devices: the Fitbit Heart Study. Circulation. 2022;146(19):1415-1424. https://doi.org/10.1161/CIRCULATIONAHA.122.060291
19. Varma N, Han JK, Passman R, et al. Promises and perils of consumer mobile technologies in cardiovascular care: JACC scientific statement. J Am Coll Cardiol. 2024;83(5):611-631. https://doi.org/10.1016/j.jacc.2023.11.024
20. Himmelreich JCL, Karregat EPM, Lucassen WAM, et al. Diagnostic accuracy of a smartphone-operated, single-lead electrocardiography device for detection of rhythm and conduction abnormalities in primary care. Ann Fam Med. 2019;17(5):403-411. https://doi.org/10.1370/afm.2438
21. Wegner FK, Kochhäuser S, Ellermann C, et al. Prospective blinded evaluation of the smartphone-based AliveCor Kardia ECG monitor for atrial fibrillation detection: the PEAK-AF study. Eur J Intern Med. 2020;73:72-75. https://doi.org/10.1016/j.ejim.2019.11.018
22. Mannhart D, Lischer M, Knecht S, et al. Clinical validation of 5 direct-to-consumer wearable smart devices to detect atrial fibrillation: BASEL Wearable Study. JACC Clin Electrophysiol. 2023;9(2):232-242. https://doi.org/10.1016/j.jacep.2022.09.011
23. Doggart P, Fisher C, Biglarbeigi P, et al. Accounting for inconclusive results and repeated testing: a framework for evaluating wearable ECG diagnostic performance with application to Apple Watch and AI-enhanced interpretation. Heart Rhythm. 2025. https://doi.org/10.1016/j.hrthm.2025.11.003
24. Gruwez H, Barthels M, Haemers P, et al. Detecting paroxysmal atrial fibrillation from an electrocardiogram in sinus rhythm: external validation of the AI approach. JACC Clin Electrophysiol. 2023;9(8 Pt 2):1771-1782. https://doi.org/10.1016/j.jacep.2023.04.008
25. Yazdi V, Kadiyala V, Chugh SS. Machine learning and arrhythmia: advances in atrial fibrillation detection and management. Curr Atheroscler Rep. 2025;27(1):10. https://doi.org/10.1007/s11883-025-01366-z
26. Khurshid S, Friedman S, Reeder C, et al. Electrocardiogram-based deep learning and clinical risk factors to predict atrial fibrillation. Circulation. 2022;145(2):122-133. https://doi.org/10.1161/CIRCULATIONAHA.121.057480
27. Yuan N, Duffy G, Dhruva SS, et al. Deep learning of electrocardiograms in sinus rhythm from US veterans to predict atrial fibrillation. JAMA Cardiol. 2023;8(12):1131-1139. https://doi.org/10.1001/jamacardio.2023.3701
28. Jabbour G, Nolin-Lapalme A, Tastet O, et al. Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores. Eur Heart J. 2024;45(46):4920-4934. https://doi.org/10.1093/eurheartj/ehae595
29. Habineza T, Ribeiro AH, Gedon D, et al. End-to-end risk prediction of atrial fibrillation from the 12-lead ECG by deep neural networks. J Electrocardiol. 2023;81:193-200. https://doi.org/10.1016/j.jelectrocard.2023.09.011
30. Brant LCC, Ribeiro AH, Eromosele OB, et al. Prediction of atrial fibrillation from the ECG in the community using deep learning: a multinational study. Circ Arrhythm Electrophysiol. 2025. https://doi.org/10.1161/circep.125.013734
31. Noseworthy PA, Attia ZI, Behnken EM, et al. Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. Lancet. 2022;400(10359):1206-1214. https://doi.org/10.1016/S0140-6736(22)01637-3
32. Christopoulos G, Graff-Radford J, Lopez CL, et al. Artificial intelligence-electrocardiography to predict incident atrial fibrillation: a population-based study. Circ Arrhythm Electrophysiol. 2020;13(12):e009355. https://doi.org/10.1161/CIRCEP.120.009355
33. Lubitz SA, Atlas SJ, Ashburner JM, et al. Screening for atrial fibrillation in older adults at primary care visits: VITAL-AF randomized controlled trial. Circulation. 2022;145(13):946-954. https://doi.org/10.1161/CIRCULATIONAHA.121.057014
34. Vedage NA, Friedman SF, Chang Y, et al. Risk-guided atrial fibrillation screening with artificial intelligence-enabled electrocardiogram models: a VITAL-AF trial analysis. J Am Coll Cardiol. 2026;87(14):1798-1813. https://doi.org/10.1016/j.jacc.2026.01.087
35. Khurshid S, Friedman SF, Al-Alusi MA, et al. Artificial intelligence-enabled analysis of handheld single-lead electrocardiograms to predict incident atrial fibrillation: an analysis of the VITAL-AF randomized trial. NPJ Digit Med. 2025;8(1):776. https://doi.org/10.1038/s41746-025-02164-2
36. Hygrell T, Viberg F, Dahlberg E, et al. An artificial intelligence-based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening. Europace. 2023;25(4):1332-1338. https://doi.org/10.1093/europace/euad036
37. Rizas KD, Freyer L, Sappler N, et al. Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial. Nat Med. 2022;28(9):1823-1830. https://doi.org/10.1038/s41591-022-01979-w
38. Seo HC, Oh S, Kim H, Joo S. ECG data dependency for atrial fibrillation detection based on residual networks. Sci Rep. 2021;11(1):18256. https://doi.org/10.1038/s41598-021-97308-1
39. Pfeifer JM, Lee G, Raghunath S, et al. Multicenter validation of an artificial intelligence-enabled ECG model to predict 1-year risk of atrial fibrillation or flutter. Heart Rhythm. 2026. https://doi.org/10.1016/j.hrthm.2026.03.1956
40. Biton S, Aldhafeeri M, Marcusohn E, et al. Generalizable and robust deep learning algorithm for atrial fibrillation diagnosis across geography, ages and sexes. NPJ Digit Med. 2023;6(1):44. https://doi.org/10.1038/s41746-023-00791-1
41. Mandrola J, Foy A, Naccarelli G. Screening for atrial fibrillation comes with many snags. JAMA Intern Med. 2018;178(10):1296-1298. https://doi.org/10.1001/jamainternmed.2018.4038
42. Patel SM, Ruff CT. Subclinical atrial fibrillation and anticoagulation: weighing the absolute risks and benefits. Circulation. 2024;149(19):1475-1477. https://doi.org/10.1161/circulationaha.123.067919
43. Boriani G, Gerra L, Mei DA, et al. Detection of subclinical atrial fibrillation with cardiac implanted electronic devices: what decision making on anticoagulation after the NOAH and ARTESiA trials? Eur J Intern Med. 2024;123:37-41. https://doi.org/10.1016/j.ejim.2024.01.002
44. Shoamanesh A, Field TS, Coutts SB, et al. Apixaban versus aspirin for stroke prevention in people with subclinical atrial fibrillation and a history of stroke or transient ischaemic attack: subgroup analysis of the ARTESiA randomised controlled trial. Lancet Neurol. 2025;24(2):123-132. https://doi.org/10.1016/s1474-4422(24)00475-7
45. Lopes RD, Atlas SJ, Go AS, et al. Effect of screening for undiagnosed atrial fibrillation on stroke prevention. J Am Coll Cardiol. 2024;84(21):2073-2084. https://doi.org/10.1016/j.jacc.2024.08.019
46. Wijesurendra R, Pessoa-Amorim G, Buck G, et al. Remote screening for asymptomatic atrial fibrillation: the AMALFI randomized clinical trial. JAMA. 2025. https://doi.org/10.1001/jama.2025.15440
47. Hussain A, Guni A, Gandhewar R, et al. Is the FDA regulation of cardiology AI devices supporting cardiovascular innovation: a scoping review. Heart. 2025;111(10):456-464. https://doi.org/10.1136/heartjnl-2025-326307
48. Armoundas AA, Avari Silva JN, Baykaner T, et al. HRS scientific statement on artificial intelligence integration framework into clinical electrophysiology workflows. Heart Rhythm. 2026. https://doi.org/10.1016/j.hrthm.2026.04.013
49. Agarwal AA, Sajjadian AN, Patel K, et al. After clearance — continuous monitoring as the foundation of clinical AI oversight. NEJM AI. 2026;3(1):AIp2500112. https://doi.org/10.1056/aie2600807
50. Joglar JA, Chung MK, Armbruster AL, et al.; Writing Committee Members. 2023 ACC/AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2024;83(1):109-279. https://doi.org/10.1016/j.jacc.2023.08.017
51. Svennberg E, Han JK, Caiani EG, et al. State of the art of artificial intelligence in clinical electrophysiology in 2025: a scientific statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology. Europace. 2025;27(5):euaf071. https://doi.org/10.1093/europace/euaf071
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Szymon Pieczara, Paulina Musiał, Wojciech Nowicki, Damian Machaj, Mikołaj Stańko, Zofia Stefanik, Piotr Urbanowicz
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The periodical offers access to content in the Open Access system under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0
Stats
Number of views and downloads: 20
Number of citations: 0
