Artificial intelligence-enabled electrocardiography (AI-ECG) is rapidly expanding, yet its real-world clinical integration remains limited by data heterogeneity, unclear workflows, and uncertainty about clinical impact. This review synthesizes evidence from pragmatic trials and prospective studies demonstrating that AI-ECG can improve early detection, enable opportunistic screening, and guide personalized care across diverse settings. We highlight persistent challenges—including bias, explainability, and regulatory adaptation—and propose practical strategies for safe, scalable deployment. By integrating clinical and technical perspectives, this review outlines how AI-ECG can evolve into a reliable digital biomarker that enhances cardiovascular care.

Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence–enabled electrocardiography (AI-ECG) models show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD would accurately detect LVSD and predict future clinical outcomes in LBBB patients.

Several artificial intelligence–enhanced electrocardiogram (AI-ECG) models have shown promise in detecting left ventricular systolic dysfunction (LVSD), but their head-to-head agreement and performance have not been independently compared within the same cohort.

Left ventricular filling pressure (LVFP) is associated with heart failure symptoms, a key prognostic marker, and a therapeutic target, but is difficult to measure non-invasively. We aimed to develop and validate a deep learning-based artificial intelligence (AI) model using a standard 12-lead electrocardiogram (ECG) to detect elevated LVFP and assess its prognostic value.

Artificial intelligence–enabled electrocardiography (AI-ECG) accurately detects left ventricular systolic dysfunction (LVSD). Prior studies suggest higher AI-ECG LVSD scores predict adverse outcomes in heart failure with reduced ejection fraction (HFrEF). Whether serial changes in these scores predict recovery to heart failure with improved ejection fraction (HFiEF) is unclear. If validated, AI-ECG could offer a non-invasive alternative to frequent transthoracic echocardiography (ECHO) for monitoring ejection fraction (EF).We investigated whether sequential 12-lead AI-ECG LVSD scores are associated with EF improvement in HFrEF, potentially enabling clinicians to detect HFiEF without relying solely on serial ECHO.This single-center, retrospective cohort study included all adults (≥19 years) with at least one ECHO-confirmed LVEF ≤40% (2017–2025). Exclusion criteria included left ventricular assist device, heart transplantation, ECMO, or IABP. Each ECG was analyzed by an AI-ECG model, yielding an LVSD probability (0.0–100.0). ECHOs within 14 days of an ECG formed ECG-ECHO pairs. Baseline HFrEF was defined by the first LVEF ≤40%. HFiEF required a ≥10% absolute increase in LVEF from baseline after ≥90 days. We defined the “Delta score” as (baseline AI-ECG probability − follow-up AI-ECG probability). Associations were assessed using Cox proportional hazards regression, adjusted for age, sex, obesity, hypertension, diabetes, and ischemic heart disease (IHD). Kaplan-Meier analysis was conducted to compare the probability of achieving HFiEF among the three distinct Delta score groups (1st, 2nd, and 3rd tertile), with statistical significance assessed using the log-rank test.Among 832 patients (mean age 64.0±14.0 years; 66.6% male), 426 (51.2%) achieved HFiEF. They were younger (62.1±13.7 vs. 66.1±14.0 years, p<0.001) and had lower IHD prevalence (38.3% vs. 58.1%, p<0.001). Baseline LVEF was lower in the HFiEF group (29.15% vs. 32.17%, p<0.001), with a significant rise at follow-up (49.89% vs. 33.26%, p<0.001). Baseline AI-ECG scores were similar (57.56 vs. 53.63, p=0.069) but dropped substantially in the HFiEF group at follow-up (19.52 vs. 48.45, p<0.001). Each 1-point higher baseline AI-ECG score predicted a 0.9% lower chance of HFiEF (aHR 0.991, p<0.001), while each 1-point increase in Delta score predicted a 3.9% higher HFiEF likelihood (aHR 1.039, p<0.001), with a significant interaction (p=0.004). Kaplan-Meier analysis demonstrated significant differences among the three distinct Delta score groups in predicting recovery to HFiEF (p < 0.0001).Baseline AI-ECG LVSD scores and their serial decreases both predict EF recovery in HFrEF. Incorporating AI-ECG into routine care could offer a simple, non-invasive strategy to track LV function improvement—complementing or reducing the need for repeated ECHO.

Recognizing the need for explainable AI (XAI) approaches to enable the successful integration of AI-based ECG prediction models(AI-ECG) into clinical practice, we introduce a framework generating CounterFactual ECGs (i,e., named CoFE) to illustrate howspecific features, such as amplitudes and intervals, influence themodel’s predictive decisions. To demonstrate the applicability ofthe CoFE, we present two case studies: atrial fibrillation classification and potassium level regression models. The CoFE revealsfeature changes in ECG signals that align with the establishedclinical knowledge. By clarifying both where valid features appear in the ECG and how they influence the model’s predictions, we anticipate that our framework will enhance the interpretability of AI-ECG models and support more effective clinicaldecision-making. Our demonstration video is available at: https://www.youtube.com/watch?v=YoW0bNBPglQ.

Artificial intelligence-enhanced electrocardiogram (AI-ECG) using single-lead ECG can detect Left Ventricular Systolic Dysfunction (LVSD). However, most models are trained on Lead I from 12-lead ECGs, which differs fundamentally from smartwatch ECGs. In real-world settings, smartwatch ECGs not only differ structurally and physically from standard ECGs but are also affected by noise and user-related artifacts. Furthermore, validation remains limited due to the scarcity of data from self-recorded smartwatch ECGs.This study investigates whether self-recorded smartwatch ECGs can reliably monitor LVSD in real-world settings by comparing AI-ECG scores with ejection fraction from echocardiography.From July to October 2024, we enrolled participants who had recently undergone or were scheduled for echocardiography. Eligible individuals were instructed to record ECGs using their smartwatches (Samsung Galaxy or Apple Watch) at least twice daily for over a week, with a paired echocardiogram performed within 14 days. We adapted our previously developed convolutional neural network-based AI-ECG model to analyze smartwatch ECGs. The model was fine-tuned using smartwatch ECG data, leveraging the foundation model architecture with an integrated preprocessing module to manage signal noise inherent to smartwatch-derived data. It outputs a score between 0 and 100, with higher scores indicating a greater likelihood of LVSD. We evaluated model performance using two approaches: (1) Approach 1: All available ECGs were analyzed individually to generate scores and assess overall performance (2) Approach 2: Three ECGs per day were randomly selected for each participant, and their median score was used as the representative value for performance evaluation. All ECGs were processed without explicit adjustment for signal noise.A total of 27 participants were included, with 77.4% using Samsung Galaxy Watches and 22.6% using Apple Watches. Echocardiography, performed at a median interval of 6 days, identified 7 participants (36.5%) with LVSD. The median AI-ECG score was 55.0 in the LVSD group and 6.5 in the non-LVSD group. Overall, 1,497 ECGs were collected, including 866 from the LVSD group. When analyzing all available ECGs, the area under the receiver operating characteristic curve (AUROC) was 0.915 (95% confidence interval: 0.900–0.927). When analyzing only three randomly selected ECGs per day, the AUROC was 0.864.Our study demonstrates that an AI-based single-lead ECG approach can reliably monitor LVSD when applied to self-recorded smartwatch data in real-world settings. These findings provide important evidence supporting the extension of our AI-ECG model to analyze smartwatch ECGs.

Left bundle branch block (LBBB) is a common electrocardiogram (ECG) abnormality associated with left ventricular systolic dysfunction (LVSD). Although artificial intelligence (AI)–driven ECG analysis shows promise for LVSD screening, it remains unclear if a general AI-ECG model or one tailored for LBBB patients yields better performance.This study evaluates 4 AI-ECG models for detecting LVSD in LBBB patients and examines the impact of training cohort definitions.We developed 4 models using 364,845 ECGs from 4 hospitals: 1) a general AI-ECG model; 2) a model trained on automatically extracted LBBB cases; 3) a model trained on a well-curated single-center LBBB data set with expert review; and 4) a hybrid model employing transfer learning by fine-tuning the general model with single-center LBBB data. LVSD was defined as an ejection fraction #40%. All models were externally validated on 1,334 ECGs from another hospital, with performance assessed by area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and predictive values.In external validation, the transfer learning model achieved the highest AUROC (0.903; 95% CI: 0.887-0.918), closely followed by the general model (0.899; 95% CI: 0.883-0.915); the difference was not significant. Models using automated or expert-based LBBB extraction had lower AUROCs (0.879 and 0.841, respectively). The general model demonstrated high sensitivity, whereas the transfer learning model exhibited superior specificity.Our findings indicate that a broad AI-ECG model reliably detects LVSD in LBBB patients, and transfer learning offers modest improvements without requiring curated LBBB data sets. Evaluating algorithms in representative clinical populations is essential.

Leveraging Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) for analyzing medical data, particularly Electrocardiogram (ECG), offers high accuracy and convenience. However, generating reliable, evidence-based results in specialized fields like healthcare remains a challenge, as RAG alone may not suffice. We propose a Zero-shot ECG diagnosis framework based on RAG for ECG analysis that incorporates expert-curated knowledge to enhance diagnostic accuracy and explainability. Evaluation on the PTB-XL dataset demonstrates the framework’s effectiveness, highlighting the value of structured domain expertise in automated ECG interpretation. Our framework is designed to support comprehensive ECG analysis, addressing diverse diagnostic needs with potential applications beyond the tested dataset.

Heart failure (HF) is a growing global health burden, yet early detection remains challenging due to the limitations of traditional diagnostic tools such as electrocardiograms (ECGs). Recent advances in deep learning offer new opportunities to identify left ventricular systolic dysfunction (LVSD), a key indicator of HF, from ECG data. This study validates AiTiALVSD, our previously developed artificial intelligence (AI)-enabled ECG Software as a Medical Device, for its accuracy, transparency, and robustness in detecting LVSD. Methods: This retrospective single-center cohort study involved patients suspected of LVSD. The AiTiALVSD model, based on a deep learning algorithm, was evaluated against echocardiographic ejection fraction values. To enhance model transparency, the study employed Testing with Concept Activation Vectors (TCAV), clustering analysis, and robustness testing against ECG noise and lead reversals. Results: The study involved 688 participants and found AiTiALVSD to have a high diagnostic performance, with an AUROC of 0.919. There was a significant correlation between AiTiALVSD scores and left ventricular ejection fraction values, confirming the model’s predictive accuracy. TCAV analysis showed the model’s alignment with medical knowledge, establishing its clinical plausibility. Despite its robustness to ECG artifacts, there was a noted decrease in specificity in the presence of ECG noise. Conclusions: AiTiALVSD’s high diagnostic accuracy, transparency, and resilience to common ECG discrepancies underscore its potential for early LVSD detection in clinical settings. This study highlights the importance of transparency and robustness in AI-ECG, setting a new benchmark in cardiac care.