Background and Objectives: The diagnostic accuracy of an artificial intelligence (AI)-derived initial 12-lead electrocardiogram (ECG) analysis was evaluated for early carbon monoxide-induced cardiomyopathy (CO-CMP) risk detection. Materials and Methods: Retrospective medical data of carbon monoxide poisoning (COP) cases between 1 January 2015 and 31 December 2024 were screened for the primary outcome: odds ratio (OR) for echocardiographically confirmed CO-CMP among those with high-risk probability score per the AI-derived model. Secondary outcomes included left ventricular ejection fraction (LVEF) and AI-derived probability score, critical care requirements, including intubation and intensive care unit (ICU) admission, and cardiac arrest events. Results: A total of 51 patients with acute COP were included in the final analysis, with 13 (25.5%) being diagnosed with CO-CMP. The LVEF in the CO-CMP group was lower than that in the non-CO-CMP group (40.00 ± 13.80% vs. 63.76 ± 6.24%, p < 0.001). The AI-derived probability score was higher in the CO-CMP group (11.3 [3.8–32.7] vs. 0.5 [0.2–2.2], p < 0.001). Among cardiac biomarkers, troponin I (2.37 [0.32–7.88] vs. 0.06 [0.06–0.95] ng/mL, p = 0.002) was higher in the CO-CMP group. Patients with CO-CMP required recurrent ventilator support (76.9% vs. 21.1%, p < 0.001) and ICU admission (92.3% vs. 42.1%, p = 0.003). In multivariable regression analysis, the AI-derived prediction model was independently associated with CO-CMP (OR 1.14; 95% confidence interval (CI) 1.02–1.27; p = 0.017; Firth-penalized OR 1.11; 95% CI 1.03–1.25; p < 0.001). Receiver operating characteristic analysis of the AI-derived model showed an area under the curve of 0.85 (95% CI 0.70–0.96) for the AI score alone and 0.92 (95% CI 0.83–0.99) for the Combined AI–cardiac marker model, with a sensitivity of 92.3% and specificity of 81.6%. Pairwise DeLong comparisons between the Combined AI model and comparator models did not reach statistical significance (Combined vs. AI-only, p = 0.092; Combined vs. cardiac markers, p = 0.052); however, the likelihood-ratio test for adding the AI probability score to the cardiac marker-only model demonstrated significant incremental information (χ2 = 13.68, p < 0.001). Conclusions: AI-based ECG analysis showed exploratory diagnostic association with LV systolic dysfunction observed in suspected CO-CMP patients. Given the limited sample size, low events-per-variable ratio, and lack of external validation, these findings suggest that AI-ECG analysis may provide incremental information for early cardiac risk stratification in selected patients.

Personalizing biosignal models is challenging due to temporal drift, where physiological signals evolve gradually or fluctuate abruptly over time. Recent studies have applied Online Test-Time Adaptation (OTTA), which continuously updates model parameters with streaming inputs, and demonstrated its promise for real-world deployment. This naturally raises the question of whether meta-learning, a related paradigm originally developed for rapid task adaptation, can also serve as an effective strategy for biosignal personalization. To address this, we systematically compare OTTA and meta-learning under identical streaming conditions for blood pressure prediction. Our analysis shows that OTTA achieves strong personalization by rapidly following distributional changes, while conventional meta-learning exhibits conservative adaptation in streaming regression tasks—avoiding large deviations in stable regimes but failing to capture gradual long-term trends. These findings suggest that meta-learning should not be treated as a direct alternative to OTTA, but rather redesigned to support continuous, long-term personalization in biosignals better.

Question
How well does an artificial intelligence electrocardiogram (AI-ECG) algorithm detect left ventricular systolic dysfunction (LVSD)?

Findings
In this cross-sectional study of 1444 participants with paired ECG and echocardiography across 8 health care facilities in Kenya, a high burden of LVSD was identified with high sensitivity and negative predictive value of the AI-ECG algorithm.

Meaning
The findings suggest that AI-ECG screening may be an effective strategy for identifying LVSD in resource-limited settings.

Aims
Vasovagal syncope (VVS) can cause injury and impaired quality of life, and effective prevention requires timely warning before loss of consciousness. To evaluate whether smartwatch photoplethysmography (PPG)-derived heart rate variability (HRV) can predict VVS before symptom onset, and to identify an optimal observation window and lead time.

Methods and results
We prospectively enrolled 132 patients with suspected neurally mediated syncope who underwent head-up tilt (HUT) testing while wearing a wrist-worn Samsung Galaxy Watch 6 for continuous multiwavelength PPG acquisition (25 Hz). The HRV features (n = 107) were extracted. An Extra Trees classifier (600 trees) was trained using an 80/20 subject-level split and evaluated on a hold-out test set. Model performance was assessed using AUROC and threshold metrics, including specificity, at a fixed sensitivity of 0.90. Sixty-three participants were HUT-positive, and 69 were HUT-negative. The 5-min presyncope window achieved the highest discrimination (AUROC, 0.91; 95% CI 0.77–1.00). At 90% sensitivity, specificity was 0.64 (95% CI 0.40–0.85). Using a fixed 5-min window, early prediction remained robust at a 5-min lead time (AUROC 0.91; 95% CI 0.76–1.00; accuracy 84.6%; 95% CI 0.65–0.92). The most informative predictors included nonlinear complexity metrics (approximate entropy and composite multiscale entropy) and autonomic balance indices (normalized low-frequency, log-transformed high-frequency, and the cardiac vagal index).

Conclusion
Artificial intelligence-enabled analysis of smartwatch PPG–derived HRV can prospectively predict VVS during HUT using a short 5-min observation window while maintaining clinically meaningful performance at a 5-min lead time, supporting the feasibility of wearable, real-time warning systems.

Background
Left ventricular filling pressure is associated with heart failure symptoms and a key prognostic marker and therapeutic target, but a scalable, accessible, and affordable tool for its noninvasive, serial estimation remains lacking. We developed an artificial intelligence (AI) model using a standard 12‐lead ECG to detect increased E/e', a general surrogate of elevated left ventricular filling pressure on echocardiography.

Methods
The AI model was built upon a foundation model trained with >1 million multiethnic ECGs and fine‐tuned through a development cohort of 225 737 ECGs and 115 982 echocardiogram records from 92 775 unique patients across 2 tertiary hospitals. The model performance was assessed in a separate internal test set (n=9278) and an independent external cohort from another tertiary hospital (n=17 926). Prognostic significance of the AI‐ECG output was evaluated in these hospital cohorts, as well as the UK Biobank (n=43 347). Finally, we validated the model output against invasively measured left ventricular filling pressure through cardiac catheterization (n=60).

ResultsThe AI‐ECG model detected increased E/e' with an area under the curve of 0.868 (95% CI, 0.859–0.877) and 0.850 (95% CI, 0.841–0.858) in the internal and external test cohorts, respectively. The AI‐ECG output value demonstrated a strong correlation with invasively measured left ventricular end‐diastolic pressure (Pearson's r=0.655) and was significantly associated with incident heart failure and mortality.

Conclusions
The AI‐ECG may enable identification of patients with increased left ventricular filling pressure and provide powerful prognostic information. Further prospective studies are warranted to evaluate its clinical utility.

Aims
Myocardial ischaemia is associated with adverse prognosis. Identifying high-risk individuals who require a stress test is challenging, and a practical screening tool to detect these patients, especially in asymptomatic individuals, is lacking. We aimed to develop an artificial intelligence (AI) model based on a resting 12-lead electrocardiogram to detect patients with inducible myocardial ischaemia.

Methods and results
An AI model was developed using 12 074 resting 12-lead ECGs from 11 700 patients and tested on 1342 patients at two hospitals. Patients with inducible ischaemia were defined as those who received revascularisation for silent ischaemia, stable angina, or unstable angina between 2004 and 2020 (n = 6070). No ischaemia group included patients with 0% stenosis in all epicardial coronary arteries and coronary artery calcium score of ≤100 in coronary computed tomography angiography (n = 7346). The primary outcome was the model performance categorising patients with inducible myocardial ischaemia. We further validated the model through multiple reference and external validation datasets encompassing 35 898 patients. The model showed an area under the receiver operating characteristic curve (AUROC) of 0.90 (95% CI 0.88─0.92), and an area under the precision-recall curve (AUPRC) of 0.87 (95% CI 0.84─0.89). The model performance was robust regardless of age, sex, comorbidities, clinical diagnosis, or culprit vessels. Consistent results were demonstrated in an age- and sex-matched dataset (n = 7414; AUROC 0.85, 95% CI 0.83─0.87 and AUPRC 0.84, 95% CI 0.82─0.87), as well as in reference and external cohorts.

Conclusion
Electrocardiogram-trained AI demonstrated favourable performance in detecting inducible myocardial ischaemia. It may enable screening and risk stratification of high-risk patients.

Background
Asymptomatic left ventricular systolic dysfunction (LVSD) is a well-established precursor of overt heart failure (HF), yet it often remains undiagnosed in the general population. Artificial intelligence–enabled electrocardiogram (ECG) analysis offers a scalable approach for early detection.

Objectives The purpose of this study was to evaluate the diagnostic performance of an artificial intelligence–enabled ECG model (AiTiALVSD) for identifying asymptomatic LVSD in a large health screening population.

Methods
In this retrospective, single-center study, we evaluated the AiTiALVSD model among 40,713 self-referred adults who underwent a total of 60,711 ECG-transthoracic echocardiography (TTE) pairs between 2011 and 2023. LVSD was defined as a left ventricular ejection fraction ≤40%. Model discrimination was assessed using the area under the receiver-operating characteristic curve (AUROC) and the area under the precision–recall curve (AUPRC), and diagnostic performance metrics were compared with established HF risk scores.

Results
Among 60,711 ECG–TTE pairs, 32 cases (0.054%) met the criteria for LVSD. The AiTiALVSD model demonstrated excellent discrimination (AUROC 0.973; AUPRC 0.328), with a sensitivity of 90.6%, specificity of 99.4%, positive predictive value of 7.7%, and a negative predictive value of 100%. Established HF risk scores, including the MESA (Multi-Ethnic Study of Atherosclerosis) 5-year HF score and Pooled Cohort Equations to Prevent HF score, showed inferior discrimination (AUROC: 0.696 and 0.672, respectively). The MESA score was not designed to detect prevalent LVSD and was calculated without natriuretic peptide data, which may have disadvantaged its performance in this comparison. Simulation analyses suggested that approximately 1,841 ECGs and 13 confirmatory TTEs would be required to detect one case of LVSD.

Conclusions
In a real-world screening population with an extremely low prevalence of LVSD, the AiTiALVSD model demonstrated high diagnostic accuracy, supporting its potential role as a rule-out screening tool for HF prevention. Prospective validation is warranted.

Electrocardiograms (ECGs) remain widely archived as paper ECG charts. In the 12-lead paper ECG chart layout, each lead shows only 2.5-second visible segments. Therefore, digitized charts are incomplete, leaving most of the 10-second recording invisible and misaligned with the digital standard required by ECG-AI models. Previous work has attempted to recover these invisible segments but has shown markedly lower performance than visible segments. We propose the Visible Context Propagation (VCP) architecture, an extension of ECGrecover, which leverages the quasi-periodic structure of ECGs and employs cross-attention to propagate contextual information from visible to invisible segments. Our model consistently outperformed ECGrecover, the strongest baseline, reducing RMSE by 32.4% overall, including 12.0% on invisible segments. Beyond recovery accuracy, evaluations on ECG applications demonstrated that recovered ECGs achieved performance comparable to raw ECGs in both diagnostic classification and ECG feature measurement. These results highlight the effectiveness of explicitly modeling the propagation of visible-to-invisible context and establish VCP as a robust solution for recovering incomplete paper-based ECGs, enabling reliable surrogates for clinical and analytical use.

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.