{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T15:05:20Z","timestamp":1789571120220,"version":"build-2803163510"},"reference-count":78,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Rhythm control therapy has an important role in atrial fibrillation (AF) management, and antiarrhythmic drugs (AADs) remain essential for pharmacological cardioversion, maintenance of sinus rhythm, reduction in AF burden, and treatment before or after catheter ablation. However, their efficacy varies substantially among patients, while proarrhythmia, organ toxicity, drug interactions, and treatment discontinuation frequently limit their use. Current drug selection therefore relies mainly on safety-based exclusion based on structural heart disease, ventricular function, coronary disease, renal or hepatic function, and baseline conduction and repolarization characteristics, rather than on individualized prediction of comparative therapeutic benefit. This narrative review examines the potential role of artificial intelligence (AI), machine learning (ML), computational electrophysiology, and cardiac digital twins across the AAD treatment pathway. Particular attention is given to patient selection, comparative drug choice, prediction of cardioversion success and sinus-rhythm maintenance, dose optimization, proarrhythmia assessment, extracardiac toxicity, and longitudinal safety surveillance. AI can potentially integrate clinical, electrocardiographic (ECG), imaging, wearable, genomic, and pharmacological data to estimate patient-specific efficacy and toxicity. ML models have already demonstrated the feasibility of predicting drug-induced QT prolongation from electronic health records and detecting ECG signatures associated with drug-induced arrhythmic risk. Moreover, patient-specific AF digital twins have been used to simulate electrophysiological responses to amiodarone and identify patients with different subsequent rhythm outcomes. Nevertheless, most available applications remain retrospective, single-center, non-comparative, or proof-of-concept, and few directly support selection among alternative AADs. Most are prognostic, estimate outcomes under observed care, or predict drug-specific toxicity; models that estimate outcomes under alternative AADs remain the essential missing element. AI-supported antiarrhythmic therapy represents a promising transition from population-based prescribing toward individualized estimation of efficacy, toxicity, and monitoring requirements. Its clinical adoption will require multicenter external validation, causal treatment-effect modeling, prospective workflow evaluation, randomized impact trials, transparent uncertainty reporting, and continued clinician oversight.<\/jats:p>","DOI":"10.3390\/medicina62091783","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T13:50:27Z","timestamp":1789566627000},"page":"1783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for Precision Antiarrhythmic Drug Therapy in Atrial Fibrillation: From Recurrence Prediction to Comparative Treatment Selection"],"prefix":"10.3390","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9763-4266","authenticated-orcid":false,"given":"Alina","family":"Scridon","sequence":"first","affiliation":[{"name":"Department of Physiology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 T\u00e2rgu Mure\u0219, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6793-9248","authenticated-orcid":false,"given":"Vasile-Bogdan","family":"Hala\u021biu","sequence":"additional","affiliation":[{"name":"Department of Physiology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 T\u00e2rgu Mure\u0219, Romania"},{"name":"Department of Cardiology, Emergency Clinical County Hospital T\u00e2rgu Mure\u0219, 540136 T\u00e2rgu Mure\u0219, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9390-7263","authenticated-orcid":false,"given":"Dan-Alexandru","family":"Cozac","sequence":"additional","affiliation":[{"name":"Department of Physiology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 T\u00e2rgu Mure\u0219, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1056\/NEJMoa2019422","article-title":"Early Rhythm-Control Therapy in Patients with Atrial Fibrillation","volume":"383","author":"Kirchhof","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3314","DOI":"10.1093\/eurheartj\/ehae176","article-title":"2024 ESC Guidelines for the management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): Developed by the task force for the management of atrial fibrillation of the European Society of Cardiology (ESC), with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Endorsed by the European Stroke Organisation (ESO)","volume":"45","author":"Rienstra","year":"2024","journal-title":"Eur. Heart J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1161\/CIR.0000000000001193","article-title":"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","volume":"149","author":"Joglar","year":"2024","journal-title":"Circulation"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1056\/NEJMoa2029980","article-title":"Cryoablation or Drug Therapy for Initial Treatment of Atrial Fibrillation","volume":"384","author":"Andrade","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1056\/NEJMoa2029554","article-title":"Cryoballoon Ablation as Initial Therapy for Atrial Fibrillation","volume":"384","author":"Wazni","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"euaf076","DOI":"10.1093\/europace\/euaf076","article-title":"Practical compendium of antiarrhythmic drugs: A clinical consensus statement of the European Heart Rhythm Association of the European Society of Cardiology","volume":"27","author":"Merino","year":"2025","journal-title":"Europace"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1038\/s41746-024-01298-z","article-title":"Clinical usefulness of digital twin guided virtual amiodarone test in patients with atrial fibrillation ablation","volume":"7","author":"Hwang","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s41073-019-0064-8","article-title":"SANRA\u2014A scale for the quality assessment of narrative review articles","volume":"4","author":"Baethge","year":"2019","journal-title":"Res. Integr. Peer Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"414","DOI":"10.4300\/JGME-D-22-00480.1","article-title":"Narrative Reviews: Flexible, Rigorous, and Practical","volume":"14","author":"Sukhera","year":"2022","journal-title":"J. Grad. Med. Educ."},{"key":"ref_10","first-page":"CD005049","article-title":"Antiarrhythmics for maintaining sinus rhythm after cardioversion of atrial fibrillation","volume":"9","author":"Valembois","year":"2019","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1056\/NEJM200003303421302","article-title":"Amiodarone to prevent recurrence of atrial fibrillation. Canadian Trial of Atrial Fibrillation Investigators","volume":"342","author":"Roy","year":"2000","journal-title":"N. Engl. J. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1111\/j.1540-8167.2010.01764.x","article-title":"A short-term, randomized, double-blind, parallel-group study to evaluate the efficacy and safety of dronedarone versus amiodarone in patients with persistent atrial fibrillation: The DIONYSOS study","volume":"21","author":"Radzik","year":"2010","journal-title":"J. Cardiovasc. Electrophysiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1093\/eurheartj\/ehv501","article-title":"Efficacy of Antiarrhythmic Drugs Short-Term Use After Catheter Ablation for Atrial Fibrillation (EAST-AF) trial","volume":"37","author":"Kaitani","year":"2016","journal-title":"Eur. Heart J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3356","DOI":"10.1093\/eurheartj\/ehu354","article-title":"Recurrence of arrhythmia following short-term oral AMIOdarone after CATheter ablation for atrial fibrillation: A double-blind, randomized, placebo-controlled study (AMIO-CAT trial)","volume":"35","author":"Darkner","year":"2014","journal-title":"Eur. Heart J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1093\/eurheartj\/ehx666","article-title":"PulmOnary vein isolation with vs. without continued antiarrhythmic Drug trEatment in subjects with Recurrent Atrial Fibrillation (POWDER AF): Results from a multicentre randomized trial","volume":"39","author":"Duytschaever","year":"2018","journal-title":"Eur. Heart J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1954","DOI":"10.1161\/01.CIR.92.7.1954","article-title":"Atrial fibrillation begets atrial fibrillation. A study in awake chronically instrumented goats","volume":"92","author":"Wijffels","year":"1995","journal-title":"Circulation"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/S0008-6363(01)00380-7","article-title":"The contribution of ionic currents to changes in refractoriness of human atrial myocytes associated with chronic atrial fibrillation","volume":"52","author":"Workman","year":"2001","journal-title":"Cardiovasc. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.hrthm.2018.07.019","article-title":"Structural remodeling and conduction velocity dynamics in the human left atrium: Relationship with reentrant mechanisms sustaining atrial fibrillation","volume":"16","author":"Honarbakhsh","year":"2019","journal-title":"Heart Rhythm"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Scridon, A., and Balan, A.I. (2022). Targeting Myocardial Fibrosis-A Magic Pill in Cardiovascular Medicine?. Pharmaceutics, 14.","DOI":"10.3390\/pharmaceutics14081599"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1001\/jama.2014.3","article-title":"Association of atrial tissue fibrosis identified by delayed enhancement MRI and atrial fibrillation catheter ablation: The DECAAF study","volume":"311","author":"Marrouche","year":"2014","journal-title":"JAMA"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1111\/jce.15794","article-title":"Left atrial epicardial adipose tissue exacerbates electrical conduction disturbance in normal-weight patients undergoing pulmonary vein isolation for atrial fibrillation","volume":"34","author":"Yamada","year":"2023","journal-title":"J. Cardiovasc. Electrophysiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1093\/europace\/euw161","article-title":"EHRA\/HRS\/APHRS\/SOLAECE expert consensus on atrial cardiomyopathies: Definition, characterization, and clinical implication","volume":"18","author":"Goette","year":"2016","journal-title":"Europace"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Scridon, A. (2022). Autonomic imbalance and atrial ectopic activity-a pathophysiological and clinical view. Front. Physiol., 13.","DOI":"10.3389\/fphys.2022.1058427"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2589","DOI":"10.1161\/01.CIR.0000068337.25994.21","article-title":"Obstructive sleep apnea and the recurrence of atrial fibrillation","volume":"107","author":"Kanagala","year":"2003","journal-title":"Circulation"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e214","DOI":"10.1161\/CIR.0000000000000905","article-title":"Drug-Induced Arrhythmias: A Scientific Statement from the American Heart Association","volume":"142","author":"Tisdale","year":"2020","journal-title":"Circulation"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1161\/01.CIR.94.10.2535","article-title":"Sex difference in risk of torsade de pointes with d,l-sotalol","volume":"94","author":"Lehmann","year":"1996","journal-title":"Circulation"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.jacc.2012.01.070","article-title":"Symptomatic response to antiarrhythmic drug therapy is modulated by a common single nucleotide polymorphism in atrial fibrillation","volume":"60","author":"Parvez","year":"2012","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1016\/j.jacc.2016.07.766","article-title":"PITX2 Modulates Atrial Membrane Potential and the Antiarrhythmic Effects of Sodium-Channel Blockers","volume":"68","author":"Syeda","year":"2016","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1093\/europace\/euu139","article-title":"Long-standing arterial hypertension is associated with Pitx2 down-regulation in a rat model of spontaneous atrial tachyarrhythmias","volume":"17","author":"Scridon","year":"2015","journal-title":"Europace"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1097\/FJC.0000000000001739","article-title":"Relevance of CYP2D6 in the Efficacy and Toxicity of Flecainide in Patients with Atrial Fibrillation: A Cohort Study","volume":"86","author":"Gil","year":"2025","journal-title":"J. Cardiovasc. Pharmacol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1177\/1074248421995348","article-title":"Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data","volume":"26","author":"Simon","year":"2021","journal-title":"J. Cardiovasc. Pharmacol. Ther."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3948","DOI":"10.1093\/eurheartj\/ehab588","article-title":"Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome","volume":"42","author":"Prifti","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1161\/CIRCULATIONAHA.125.077494","article-title":"Deep Learning-Based Continuous QT Monitoring to Identify High-Risk Prolongation Events After Class III Antiarrhythmic Initiation","volume":"153","author":"Ansari","year":"2026","journal-title":"Circulation"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1111\/pace.14951","article-title":"Evaluation of an algorithm-guided photoplethysmography for atrial fibrillation burden using a smartwatch","volume":"47","author":"Zhao","year":"2024","journal-title":"Pacing Clin. Electrophysiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2408","DOI":"10.1016\/j.hrthm.2024.10.015","article-title":"Unipolar voltage electroanatomic mapping detects structural atrial remodeling identified by LGE-MRI","volume":"22","author":"Ali","year":"2025","journal-title":"Heart Rhythm"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Balan, A.I., Hala\u0163iu, V.B., Com\u0219ulea, E., Mutu, C.C., Cozac, D.A., Aspru, I., P\u0103curar, D., B\u0103nescu, C., Perian, M., and Scridon, A. (2025). The Diagnostic and Predictive Potential of miR-328 in Atrial Fibrillation: Insights from a Spontaneously Hypertensive Rat Model. Int. J. Mol. Sci., 26.","DOI":"10.3390\/ijms26073049"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1177\/0272989X241263356","article-title":"Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment","volume":"44","author":"Zhang","year":"2024","journal-title":"Med. Decis. Mak."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"AIoa2401033","DOI":"10.1056\/AIoa2401033","article-title":"An Electrocardiogram Foundation Model Built on over 10 Million Recordings","volume":"2","author":"Li","year":"2025","journal-title":"NEJM AI"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kompa, B., Snoek, J., and Beam, A.L. (2021). Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures. Entropy, 23.","DOI":"10.3390\/e23121608"},{"key":"ref_40","first-page":"e078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ. Clin. Res. Ed."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1016\/j.jacc.2020.04.065","article-title":"Recurrence of Atrial Fibrillation After Catheter Ablation or Antiarrhythmic Drug Therapy in the CABANA Trial","volume":"75","author":"Poole","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e29225","DOI":"10.2196\/29225","article-title":"Machine Learning Methodologies for Prediction of Rhythm-Control Strategy in Patients Diagnosed with Atrial Fibrillation: Observational, Retrospective, Case-Control Study","volume":"9","author":"Kim","year":"2021","journal-title":"JMIR Med. Inform."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1056\/NEJMoa041233","article-title":"Outpatient treatment of recent-onset atrial fibrillation with the \u201cpill-in-the-pocket\u201d approach","volume":"351","author":"Alboni","year":"2004","journal-title":"N. Engl. J. Med."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sanchez de la Nava, A.M., Arenal, \u00c1., Fern\u00e1ndez-Avil\u00e9s, F., and Atienza, F. (2021). Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population of Models Approach. Front. Physiol., 12.","DOI":"10.3389\/fphys.2021.768468"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1056\/NEJMoa0803778","article-title":"Effect of dronedarone on cardiovascular events in atrial fibrillation","volume":"360","author":"Hohnloser","year":"2009","journal-title":"N. Engl. J. Med."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Nu\u00f1ez-Garcia, J.C., S\u00e1nchez-Puente, A., Sampedro-G\u00f3mez, J., Vicente-Palacios, V., Jim\u00e9nez-Navarro, M., Oterino-Manzanas, A., Jim\u00e9nez-Candil, J., Dorado-Diaz, P.I., and S\u00e1nchez, P.L. (2022). Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model. J. Clin. Med., 11.","DOI":"10.3390\/jcm11092636"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"677","DOI":"10.4070\/kcj.2023.0012","article-title":"Machine Learning Prediction for the Recurrence After Electrical Cardioversion of Patients with Persistent Atrial Fibrillation","volume":"53","author":"Kwon","year":"2023","journal-title":"Korean Circ. J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/S0140-6736(12)60570-4","article-title":"Short-term Versus Long-term Antiarrhythmic Drug Treatment After Cardioversion of Atrial Fibrillation (Flec-SL): A Prospective, Randomised, Open-label, Blinded Endpoint Assessment Trial","volume":"380","author":"Kirchhof","year":"2012","journal-title":"Lancet"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.hroo.2025.12.019","article-title":"Multicenter analysis of device-detected atrial fibrillation burden reduction after antiarrhythmic drug initiation","volume":"7","author":"Schwartz","year":"2026","journal-title":"Heart Rhythm O2"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1038\/s41746-024-01370-8","article-title":"Fast and accurate prediction of drug induced proarrhythmic risk with sex specific cardiac emulators","volume":"7","author":"Zingaro","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1016\/j.hrthm.2025.10.045","article-title":"Machine learning\u2013enabled assessment of risk of drug-induced QT prolongation at the time of prescribing","volume":"23","author":"Jing","year":"2026","journal-title":"Heart Rhythm"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.jacep.2022.09.021","article-title":"Development of an AI-Driven QT Correction Algorithm for Patients in Atrial Fibrillation","volume":"9","author":"Tarabanis","year":"2023","journal-title":"JACC Clin. Electrophysiol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e43734","DOI":"10.2196\/43734","article-title":"Explainable Machine Learning Techniques to Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study with External Validation","volume":"25","author":"Lu","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension","volume":"26","author":"Liu","year":"2020","journal-title":"Nat. Med."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Das\u00ed, A., Roy, A., Sachetto, R., Camps, J., Bueno-Orovio, A., and Rodriguez, B. (2022). In-silico drug trials for precision medicine in atrial fibrillation: From ionic mechanisms to electrocardiogram-based predictions in structurally-healthy human atria. Front. Physiol., 13.","DOI":"10.3389\/fphys.2022.966046"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2417","DOI":"10.1038\/s41598-020-59372-x","article-title":"In situ procedure for high-efficiency computational modeling of atrial fibrillation reflecting personal anatomy, fiber orientation, fibrosis, and electrophysiology","volume":"10","author":"Lim","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hwang, I., Jin, Z., Park, J.-W., Kwon, O.-S., Lim, B., Hong, M., Kim, M., Yu, H.-T., Kim, T.-H., and Uhm, J.-S. (2021). Computational Modeling for Antiarrhythmic Drugs for Atrial Fibrillation According to Genotype. Front. Physiol., 12, Correction in Front. Physiol.\u00a02022, 13, 991997.","DOI":"10.3389\/fphys.2021.650449"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1002\/cpt.3159","article-title":"Using Machine Learning to Individualize Treatment Effect Estimation: Challenges and Opportunities","volume":"115","author":"Curth","year":"2024","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"EVIDoa2300041","DOI":"10.1056\/EVIDoa2300041","article-title":"Individualized Treatment Effect Prediction with Machine Learning\u2014Salient Considerations","volume":"3","author":"Desai","year":"2024","journal-title":"NEJM Evid."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"111538","DOI":"10.1016\/j.jclinepi.2024.111538","article-title":"Machine learning approaches to evaluate heterogeneous treatment effects in randomized controlled trials: A scoping review","volume":"176","author":"Inoue","year":"2024","journal-title":"J. Clin. Epidemiol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"28","author":"Vasey","year":"2022","journal-title":"Nat. Med."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Antoun, I., Abdelrazik, A., Eldesouky, M., Li, X., Layton, G.R., Zakkar, M., Somani, R., and Ng, G.A. (2025). Artificial intelligence in atrial fibrillation: Emerging applications, research directions and ethical considerations. Front. Cardiovasc. Med., 12.","DOI":"10.3389\/fcvm.2025.1596574"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1093\/aje\/kwv254","article-title":"Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available","volume":"183","author":"Robins","year":"2016","journal-title":"Am. J. Epidemiol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"euaf019","DOI":"10.1093\/europace\/euaf019","article-title":"Atrial fibrillation burden in clinical practice, research, and technology development: A clinical consensus statement of the European Society of Cardiology Council on Stroke and the European Heart Rhythm Association","volume":"27","author":"Doehner","year":"2025","journal-title":"Europace"},{"key":"ref_65","first-page":"e082505","article-title":"PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","year":"2025","journal-title":"BMJ. Clin. Res. ed."},{"key":"ref_66","first-page":"e081554","article-title":"FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare","volume":"388","author":"Lekadir","year":"2025","journal-title":"BMJ. Clin. Res. Ed."},{"key":"ref_67","unstructured":"(2026, May 02). Health C for D and R. Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions [Internet]. FDA, Available online: https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/marketing-submission-recommendations-predetermined-change-control-plan-artificial-intelligence."},{"key":"ref_68","unstructured":"Medical Device Coordination Group (2025). MDCG 2025-6: FAQ on the Interplay Between the Medical Devices Regulation, the In Vitro Diagnostic Medical Devices Regulation and the Artificial Intelligence Act, European Commission."},{"key":"ref_69","unstructured":"(2026, May 02). Regulation (EU) 2026\/1744 of the European Parliament and of the Council of 8 July 2026 amending Regulations (EU) 2024\/1689, (EU) 2018\/1139 and (EU) 2023\/1230 as Regards the Simplification of the Implementation of Harmonised Rules on Artificial Intelligence (Digital Omnibus on AI) (Text with EEA Relevance) [Internet]. Available online: http:\/\/data.europa.eu\/eli\/reg\/2026\/1744\/oj."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"e433","DOI":"10.1161\/CIR.0000000000001400","article-title":"Pragmatic Approaches to the Evaluation and Monitoring of Artificial Intelligence in Health Care: A Science Advisory from the American Heart Association","volume":"152","author":"Jain","year":"2025","journal-title":"Circulation"},{"key":"ref_71","unstructured":"Richardson, A. (2026, May 02). Evidence standards framework (ESF) for Digital Health Technologies [Internet]. NICE Website: The National Institute for Health and Care Excellence. NICE. Available online: https:\/\/www.nice.org.uk\/what-nice-does\/digital-health\/evidence-standards-framework-esf-for-digital-health-technologies."},{"key":"ref_72","unstructured":"Whitehead, M., Ali, R., Carrol, E., Holmes, C., and Kee, F. (2024). Equity in Medical Devices: Independent Review."},{"key":"ref_73","unstructured":"U.S. Food and Drug Administration (2025). Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations\u2014Draft Guidance."},{"key":"ref_74","unstructured":"European Parliament and Council of the European Union (2026, May 02). Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300\/2008, (EU) No 167\/2013, (EU) No 168\/2013, (EU) 2018\/858, (EU) 2018\/1139 and (EU) 2019\/2144 and Directives 2014\/90\/EU, (EU) 2016\/797 and (EU) 2020\/1828 (Artificial Intelligence Act). Off J Eur Union. 2024;L:2024\/1689. Available online: http:\/\/data.europa.eu\/eli\/reg\/2024\/1689\/oj."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Li, J., Xiang, W., Shang, D., Li, S., and Li, Q. (2026). Federated learning for cardiovascular disease prediction: A systematic review of clinical applications, validation, and translation readiness. Front. Cardiovasc. Med., 13.","DOI":"10.3389\/fcvm.2026.1831342"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"101419","DOI":"10.1016\/j.xcrm.2024.101419","article-title":"Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture","volume":"5","author":"Teo","year":"2024","journal-title":"Cell Rep. Med."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1038\/s41591-024-02902-1","article-title":"Causal machine learning for predicting treatment outcomes","volume":"30","author":"Feuerriegel","year":"2024","journal-title":"Nat. Med."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1038\/s41591-020-1034-x","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension","volume":"26","author":"Liu","year":"2020","journal-title":"Nat. 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