In conclusion, AI-derived risk estimates improved physician risk discrimination in a structured simulated survey, particularly in non-specialist settings, supporting their potential role as a digital decision-support tool.
Abstract
Atrial fibrillation (AF) is frequently asymptomatic and often remains undetected until complications arise. Although artificial intelligence (AI)-enabled electrocardiography (ECG) can predict incident AF from sinus rhythm ECGs, its influence on physician risk assessment in simulated clinical settings remains uncertain. We developed a deep learning model to predict multi-day AF risk using non-AF 12-lead ECGs. For ECG-labeled outcomes, the model achieved an AUROC of 0.79 in the internal EUMC cohort and 0.74 in the external BIDMC cohort. For Holter-labeled outcomes, AUROC values reached 0.87 in the internal EUMC subset and 0.75 in the prospective PROVISION-AF cohort. To assess decision-support utility, a multinational survey of 70 physicians evaluated how AI-derived risk estimates influenced physician risk assessment and follow-up decisions in structured simulated cases. AI assistance significantly improved physicians' AF risk discrimination (AUROC 0.573 to 0.650) and negative predictive value (0.764 to 0.839), with significant net reclassification improvement for non-AF cases (NRI 0.127, p < 0.001). Performance gains were most notable among non-electrophysiologist cardiologists. In conclusion, AI-derived risk estimates improved physician risk discrimination in a structured simulated survey, particularly in non-specialist settings, supporting their potential role as a digital decision-support tool. Further real-world implementation studies are needed to determine whether these effects translate into improved clinical outcomes or healthcare efficiency.
AI-ECG provides rapid, low-cost AF risk estimation from a single sinus rhythm ECG; however, its predictive performance is modest compared with clinical scores.
Kouki Matsuo, Y. Sobue, T. Miyake et al.· JACC: Advances· 0 citations
In patients with cryptogenic stroke receiving an ICM, the ECG-AI score showed modest discrimination for AF detection, outperforming CHA2DS2-VA and HAVOC, but not Brown ESUS-AF, which indicates a possible role for AI-driven ECG analysis in risk stratification.
F. Wouters, M. Barthels, J. Vranken et al.· Digital Health· 0 citations
CIED‐detected AF burden is strongly associated with progression to persistent AF, and ML‐based analysis of 6‐month device data enables accurate, point‐in‐time risk stratification to support earlier and more targeted clinical management.
A. Nakonechnyi, Shaul Geliaks, I. Goldenberg et al.· Annals of Noninvasive Electr...· 0 citations
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and is associated with substantial morbidity, including ischemic stroke, heart failure, and cognitive decline. Despite established diagnostic and therapeutic strategies, AF remains frequently underdiagnosed and suboptimally managed, particularly in asymptomatic or paroxysmal cases, in which the episodic nature of the arrhythmia may make it difficult to detect using standard electrocardiography. Artificial intelligence (AI), including machine learning and deep learning, has emerged as a transformative technology across multiple aspects of AF care. AI-based electrocardiographic analysis and wearable technologies have demonstrated promising performance in detecting subclinical AF and facilitating scalable population screening. Multimodal models integrating clinical, imaging, and electrophysiological data have demonstrated improved accuracy in stroke prediction, recurrence risk estimation, and therapeutic planning. AI-assisted imaging has advanced atrial segmentation, fibrosis characterization, and ablation planning, whereas AI-driven mapping systems may enhance procedural efficiency and improve patient selection. Nevertheless, significant challenges remain, including limited external validation, data heterogeneity, concerns regarding interpretability, integration into clinical workflows, and ethical and regulatory considerations. Future efforts should prioritize explainable, clinically validated, and human-centered AI systems that are integrated into real-world clinical workflows. This review provides a comprehensive overview of current AI applications in AF management, including early detection, risk stratification, cardiovascular imaging, clinical decision support, and interventional electrophysiology.
V. Cicek, M. Hayıroğlu, Vanshali Sharma et al.· Balkan Medical Journal· 0 citations
The EHR-based machine learning model, FIND-AF 2.0, identifies a high-risk subpopulation for AF diagnosis among patients at elevated risk of stroke and could enable scalable, EHR-driven, risk-guided AF screening.
R. Nadarajah, Jianhua Wu, A. Wahab et al.· Circulation· 0 citations
Artificial intelligence-enabled electrocardiogram (ECG) analysis has expanded across cardiovascular diagnosis, monitoring, and risk prediction, but published studies vary in methodology, validation, and clinical applicability. We systematically reviewed AI applications in ECG interpretation for cardiovascular diagnosis and risk prediction to define the evidence available through May 2026.
This systematic review followed PRISMA 2020. PubMed and the Cochrane Library were searched from database inception through May 2026 for original human studies evaluating artificial intelligence, machine learning, or deep learning applications in ECG-based cardiovascular detection, classification, diagnosis, monitoring, or risk prediction. Risk of bias was assessed using PROBAST. Because of substantial heterogeneity in study populations, ECG modalities, model architectures, validation strategies, and reported outcomes, findings were synthesized narratively.
A total of 108 studies were included. Arrhythmia detection, particularly atrial fibrillation (AF), was the most mature and frequently evaluated application domain. Benchmark-based studies commonly reported high apparent performance, whereas larger clinical cohorts and externally validated studies generally showed more moderate but more clinically credible estimates. The most common methodological limitations were concentrated in the analysis domain, particularly limited external validation, overfitting risk, unclear train-test separation, class imbalance, and incomplete reporting of calibration and robustness.
AI-enabled ECG interpretation shows strongest support for arrhythmia detection and automated ECG classification, while structural disease screening and prognostic modeling remain promising but less mature. Future studies should prioritize prospective, multicenter, externally validated, and workflow-integrated designs with transparent reporting, calibration assessment, cost-effectiveness evaluation, and equitable assessment across diverse populations.
Mohamed O Elhussain, Esra M Abdalla, Ragda Ali et al.· BMC Cardiovascular Disorders· 0 citations
