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10-Year Risk Equations for Incident Heart Failure in the General Population

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Point-of-Care Screening for Heart Failure with Reduced Ejection Fraction Using Artificial Intelligence during ECG-Enabled Stethoscope Examination in London, UK: A Prospective, Observational, Multicentre Study

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Phenotypic Refinement of Heart Failure in a National Biobank Facilitates Genetic Discovery

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Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images

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BNP and NT-proBNP Predict Echocardiographic Severity of Diastolic Dysfunction

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Population Risk Prediction Models for Incident Heart Failure

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Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices

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Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2 s of ECG signals

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The role of wearables in heart failure

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Predicting and preventing heart failure in type 2 diabetes

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Disparity in the Setting of Incident Heart Failure Diagnosis

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Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study

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Engaging Black Churches to Address Cancer Health Disparities: Project CHURCH

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Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study

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High-Sensitivity Cardiac Troponin I Levels and Prediction of Heart Failure

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Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images

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Cost-effectiveness of Screening for Atrial Fibrillation Using Wearable Devices

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Improving the Characterization of Stage A and B Heart Failure by Adding Global Longitudinal Strain

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Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning

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Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record

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Biomarker profiling for risk of future heart failure (HFpEF) development

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Natural language processing of clinical documentation to assess functional status in patients with heart failure

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Prediction of Incident Heart Failure by Serum Amino-Terminal Pro-B-Type Natriuretic Peptide Level in a Community-Based Cohort

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Adiposity, fat-free mass and incident heart failure in 500 000 individuals

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Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing

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Cost-effectiveness and screening performance of ECG handheld machine in a population screening programme: The Belgian Heart Rhythm Week screening programme

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Development and multinational validation of an ensemble deep learning algorithm for detecting and predicting structural heart disease using noisy single-lead electrocardiograms

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Trends in Consumer Wearable Devices With Cardiac Sensors in a Primary Care Cohort

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Wearables in Cardiovascular Disease

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Prediction Models for Heart Failure in the Community: A Systematic Review and Meta-Analysis

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Shaping the future of cardiovascular medicine in the new era of wearable devices

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Burden of Heart Failure In Latin America: A Systematic Review And Meta-Analysis

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Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm

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Artificial Intelligence-Based Identification of Left Ventricular Systolic Dysfunction from 12-Lead Electrocardiograms: External Validation and Advanced Application of an Existing Model

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ECG performance in simultaneous recordings of five wearable devices using a new morphological noise-to-signal index and Smith-Waterman-based RR interval comparisons

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Use of Smart Devices to Track Cardiovascular Health Goals in the United States

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A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers

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Development and multinational validation of an algorithmic strategy for high Lp(a) screening

Aminorroaya, A., Dhingra, L. S., Oikonomou, E. K., Saadatagah, S., Thangaraj, P., Shankar, S. V., Spatz, E. S., & Khera, R. (2024) · Nature Cardiovascular Research · doi:10.1038/s44161-024-00469-1 · Cited by 16 · see citing works

Racial Disparities in Type of Heart Failure and Hospitalization

Lee, W., Serag, H., Ohsfeldt, R. L., Eschbach, K., Khalife, W. I., Morsy, M., Smith, K. D., & Raimer, B. G. (2018) · Journal of Immigrant and Minority Health · doi:10.1007/s10903-018-0727-4 · Cited by 15 · see citing works

Performance of contemporary cardiovascular risk stratification scores in Brazil: an evaluation in the ELSA-Brasil study

Pedroso, A. F., Barreto, S. M., Brant, L. C. C., Ribeiro, A. L. P., Dhingra, L. S., Aminorroaya, A., Bittencourt, M. S., de Figueiredo, R. C., & Khera, R. (2024) · Open Heart · doi:10.1136/openhrt-2024-002762 · Cited by 15 · see citing works

SEVERE MITRAL STENOSIS AND PULMONARY HYPERTENSION IN PREGNANCY: WHEN TO ACT

Erinne, I., Alam, A., Huang, M. S., Mody, K. P., Ghaly, A., & Iyer, D. B. (2019) · Journal of the American College of Cardiology · doi:10.1016/s0735-1097(19)32994-8 · Cited by 14 · see citing works

Cost-Effectiveness of Portable Electrocardiogram for Screening Cardiovascular Diseases at a Primary Health Center in Ahmedabad District, India

Shah, K. H., Pandya, A., Kotwani, P., Saha, S., Desai, C., Tyagi, K., Saxena, D. B., Puwar, T. I., & Gaidhane, S. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.753443 · Cited by 14 · see citing works

Deep Learning-enabled Detection of Aortic Stenosis from Noisy Single Lead ElectrocardiogramsPreprint

Aminorroaya, A., Dhingra, L. S., Sangha, V., Oikonomou, E. K., Khunte, A., Shankar, S. V., Pedroso, A. F., Haynes, N., Hofer, I. S., Ouyang, D., et al. (2023) · medRxiv · doi:10.1101/2023.09.29.23296310 · Cited by 13 · see citing works

Comparing and contrasting risk factors for heart failure in patients with and without history of myocardial infarction: data from HOMAGE and the UK Biobank

Rastogi, T., Ho, F. K. W., Rossignol, P., Merkling, T., Butler, J., Clark, A. L., Collier, T. J., Delles, C., Jukema, J. W., Heymans, S. R. B., et al. (2022) · European Journal of Heart Failure · doi:10.1002/ejhf.2495 · Cited by 12 · see citing works

CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device ElectrocardiogramsPreprint

Shankar, S. V., Oikonomou, E. K., & Khera, R. (2023) · medRxiv · doi:10.1101/2023.10.02.23296404 · Cited by 11 · see citing works

Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational StudyPreprint

Dhingra, L. S., Aminorroaya, A., Sangha, V., Pedroso, A. F., Asselbergs, F. W., Brant, L. C. C., Barreto, S. M., Ribeiro, A. L. P., Krumholz, H. M., Oikonomou, E. K., et al. (2024) · medRxiv · doi:10.1101/2024.04.02.24305232 · Cited by 9 · see citing works

An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHDPreprint

Dhingra, L. S., Aminorroaya, A., Sangha, V., Pedroso, A. F., Shankar, S. V., Coppi, A. C., Foppa, M., Brant, L. C. C., Barreto, S. M., Ribeiro, A. L. P., et al. (2024) · medRxiv · doi:10.1101/2024.10.06.24314939 · Cited by 7 · see citing works

Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images

Dhingra, L. S., Aminorroaya, A., Sangha, V., Pedroso, A. F., Shankar, S. V., Coppi, A., Foppa, M., Brant, L. C. C., Barreto, S. M., Ribeiro, A. L. P., et al. (2025) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2025.01.030 · Cited by 53 · see citing works

Leveraging AI-enhanced digital health with consumer devices for scalable cardiovascular screening, prediction, and monitoring

Pedroso, A. F., & Khera, R. (2025) · npj Cardiovascular Health · doi:10.1038/s44325-025-00071-9 · Cited by 46 · see citing works

Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention

Bartusik‐Aebisher, D., Rogóż, K., & Aebisher, D. (2025) · Biomedicines · doi:10.3390/biomedicines13071685 · Cited by 40 · see citing works

Artificial intelligence in cardiovascular diagnostics: a systematic review and descriptive analysis of clinical applications and diagnostic performance

Niazai, A., Jamil, H., Hameed, M., Sheikh, S., & Nisar, M. R. (2025) · BMC Cardiovascular Disorders · doi:10.1186/s12872-025-05327-x · Cited by 25 · see citing works

AHA/ACC/ESC/WHF Expert Consensus Document: Second Universal Definition of Heart Failure (2026)

Walsh, M. N., Køber, L., Sliwa, K. S., Adamo, M., Agarwal, A., Banerjee, A., Bozkurt, B., Čikeš, M., Damasceno, A., Desai, A. S., et al. (2026) · Circulation · doi:10.1161/cir.0000000000001455 · Cited by 21 · see citing works

AHA/ACC/ESC/WHF Expert Consensus Document: Second Universal Definition of Heart Failure (2026)

Walsh, M. N., Køber, L., Sliwa, K. S., Adamo, M., Agarwal, A., Banerjee, A., Bozkurt, B., Čikeš, M., Damasceno, A., Desai, A. S., et al. (2026) · European Heart Journal · doi:10.1093/eurheartj/ehag500 · Cited by 19 · see citing works

Predicting Heart Failure From 12-Lead ECGs Using AI

Desai, A. S., Pandey, A., Suratekar, R., Ahmad, F. S., Alger, H. M., Anto, A. G., Nujum G, S., Lopez-Jimenez, F., Oh, J. K., Asfahan, S., et al. (2026) · JACC · doi:10.1016/j.jacc.2025.10.065 · Cited by 16 · see citing works

Artificial Intelligence for Cardiovascular Care in Action

Biswas, D., Thangaraj, P., Pedroso, A. F., Ogunniyi, M. O., Maddox, T. M., Ahmad, F. S., & Khera, R. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.102307 · Cited by 15 · see citing works

Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data

Pedroso, A. F., Schwamm, L. H., & Khera, R. (2025) · Stroke · doi:10.1161/strokeaha.125.050447 · Cited by 13 · see citing works

Use of Predicted Risk and Expected Benefit to Guide Decision-Making in Cardiovascular-Kidney-Metabolic Syndrome for the Primary Prevention of Cardiovascular Disease: A Scientific Statement From the American Heart Association and American College of Cardiology

Khan, S. S., Bhave, N. M., Blumenthal, R. S., Coresh, J., Huang, X., Joseph, J. J., Khera, A., Ho, J. E., Lloyd‐Jones, D. M., Wang, C. C. L., et al. (2026) · Circulation · doi:10.1161/cir.0000000000001447 · Cited by 10 · see citing works

Precision cardiovascular medicine with big data and AI

Xu, Q., Li, Y., Zhu, M., Cai, Y., Cheng, X., Wan, M., Ju, J., Xu, Y., Li, Y., & Liu, Y. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02538-0 · Cited by 8 · see citing works

National Patterns of Remote Patient Monitoring Service Availability at US Hospitals

Pedroso, A. F., Lin, Z., Ross, J. S., & Khera, R. (2025) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.125.012034 · Cited by 8 · see citing works

Digital health technologies in medicine: evidence, artificial intelligence integration, and ethical challenges

Cascella, M., Pane, C., Pumpo, M. D., Sebastiani, E., Bussa, M., Tagliaferri, A., Brunetti, B., Meloni, P., Bianchini, E., Graziani, P., et al. (2026) · Infectious Agents and Cancer · doi:10.1186/s13027-026-00737-8 · Cited by 7 · see citing works

Cardiovascular risk stratification without recalibration: A comparative study of the PREVENT and WHO risk scores in a multiethnic Brazilian cohort

Pedroso, A. F., Brant, L. C., Ribeiro, A. L. P., Barreto, S. M., Figueiredo, R. C. C., & Khera, R. (2025) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2025.101392 · Cited by 6 · see citing works

Precision Medicine for Electrocardiogram Interpretation: Clinical Relevance, Challenges, and Advances

Namjouyan, K., Sejdić, E., Link, M. S., Pelliccia, A., Glicksberg, B. N., Trayanova, N. A., & Krittanawong, C. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm47007 · Cited by 6 · see citing works

Beyond Traditional Screening: The Future of Heart Failure Detection With Biomarkers and AI

Fang, X., Xu, Y., Zhao, Y., Li, L. L., Cheng, Z., Zhang, Z., Luo, Y., Liu, Q., Chen, G., & Shen, S. (2026) · iNew Medicine · doi:10.1002/inm3.70051 · Cited by 6 · see citing works

Heart Failure Prevention

Butler, J., Khan, M. S., Ferreira, J. P., Girerd, N., Gulati, M., Lala, A., Petrie, M. C., Stockbridge, N., Vaduganathan, M., & Zannad, F. M. (2026) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2025.12.029 · Cited by 5 · see citing works

Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review

Osonuga, A., Olawade, D. B., Gore, M., Rotifa, O. B., Egbon, E., Chinwah, V., & Boussios, S. (2025) · Annals of Translational Medicine · doi:10.21037/atm-25-62 · Cited by 4 · see citing works

ECGomics: An Open Platform for AI-ECG Digital Biomarker Discovery

Zhang, D., Li, J., Geng, S., Wang, Y., Chen, S., Fan, S., Zhao, Q., & Hong, S. (2026) · Health Data Science · doi:10.34133/hds.0427 · Cited by 4 · see citing works

Multimodal heart failure prediction model based on graph convolutional neural network

Chen, Y., Liang, X., Qi, P., Xia, & Tong, J. (2026) · Biomedical Engineering Letters · doi:10.1007/s13534-026-00560-5 · Cited by 3 · see citing works

Use of Predicted Risk and Expected Benefit to Guide Decision-Making in Cardiovascular-Kidney-Metabolic Syndrome for the Primary Prevention of Cardiovascular Disease

Khan, S. S., Ndumele, C. E., Bhave, N. M., Blumenthal, R. S., Coresh, J., Huang, X., Joseph, J. J., Khera, A., Ho, J. E., Lloyd‐Jones, D. M., et al. (2026) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2026.05.007 · Cited by 3 · see citing works

Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges

Biswas, D., Aminorroaya, A., Croon, P. M., Batinica, B., Pedroso, A. F., & Khera, R. (2025) · Current Atherosclerosis Reports · doi:10.1007/s11883-025-01337-4 · Cited by 3 · see citing works

Advances in Diagnosis and Treatment of Acute and Chronic Heart Failure: A Comprehensive Review

Kenyon, C. R., Wyk, L. V., Flom, A. P., Ibrahim, R., Pham, H. N., Lakhdar, S., Iftikhar, M., & Abdelnabi, M. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15020618 · Cited by 2 · see citing works

Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification

Dhingra, L. S., Croon, P. M., Batinica, B., Aminorroaya, A., Pedroso, A. F., & Khera, R. (2026) · Current Heart Failure Reports · doi:10.1007/s11897-026-00748-x · Cited by 2 · see citing works

Development and Validation of the AI-HeartAge Model in Framingham and UK Biobank

Korzinski, T. J., Prescott, B. R., Hamel‐Sellman, D. J., Xanthakis, V., Cooper, L. L., Hamburg, N. M., Tsao, C. W., Benjamin, E. J., Vasan, R. S., & Mitchell, G. F. (2026) · Hypertension · doi:10.1161/hypertensionaha.125.26209 · Cited by 2 · see citing works

Wearable-Echo-FM: an ECG echo foundation model for 1-lead electrocardiography

Knight, E., Oikonomou, E. K., Aminorroaya, A., Pedroso, A. R., & Khera, R. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag049 · Cited by 2 · see citing works

Wearable Devices and Data Sharing in the US

Pedroso, A. F., Dhingra, L. S., Aminorroaya, A., & Khera, R. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.17733 · Cited by 2 · see citing works

Artificial Intelligence Electrocardiogram Risk Tool—Reply

Dhingra, L. S., Aminorroaya, A., & Khera, R. (2025) · JAMA Cardiology · doi:10.1001/jamacardio.2025.4246 · Cited by 1 · see citing works

Multi-scale heart simulation augments the explainability of artificial intelligence-enabled electrocardiogram through provision of an electrocardiogram database labelled with cellular pathologies

Okada, J., Fujiu, K., Hasumi, E., Chen, Y., Washio, T., Hisada, T., & Sugiura, S. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109247 · Cited by 1 · see citing works

The Evolving Role of Artificial Intelligence and Machine Learning in the Wearable Electrocardiogram: A Primer on Wearable-Enabled Prediction of Cardiac Dysfunction

Dave, A., Dave, A., & Moussa, I. D. (2026) · Bioengineering · doi:10.3390/bioengineering13020167 · Cited by 1 · see citing works

Development and validation of an integrated prognostic model for all-cause mortality in heart failure: a comprehensive analysis combining clinical, electrocardiographic, and echocardiographic parameters

Li, Y., Xu, J., Liu, X., Wang, X., Zhao, C., & He, K. (2025) · BMC Cardiovascular Disorders · doi:10.1186/s12872-025-04642-7 · Cited by 1 · see citing works

Use of Artificial Intelligence for Detecting Left Ventricular Dysfunction and Predicting Incident Heart Failure Risk

Węgrzyn‐Witek, A., Przewłocka‐Kosmala, M., Kosmala, W., & Marwick, T. H. (2025) · ESC Heart Failure · doi:10.1002/ehf2.15442 · Cited by 1 · see citing works

Artificial intelligence in heart failure

Li, X., Liu, Y., Zhang, X., Yang, N., Xu, T. W., Cui, X. W., & Chen, G. (2026) · The Egyptian Heart Journal · doi:10.1186/s43044-026-00723-w · Cited by 1 · see citing works

Prediction of Heart Failure With Reduced Ejection Fraction With Artificial Intelligence Electrocardiography in Patients With Atrial Fibrillation

Shropshire, S. J., Manemann, S. M., Jiang, R., Vaughan, L. E., Noseworthy, P. A., Siontis, K. C., Attia, Z. I., Gersh, B. J., & Chamberlain, A. M. (2026) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2026.100347 · Cited by 1 · see citing works

The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation

Croon, P. M., Dhingra, L. S., Pedroso, A. F., & Khera, R. (2026) · Current Heart Failure Reports · doi:10.1007/s11897-026-00764-x · Cited by 1 · see citing works

Artificial intelligence and the evolution of the electrocardiogram: from cardiovascular diagnostic tool to digital biomarker

Choi, R. B., & Khera, R. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag091 · Cited by 1 · see citing works

Comments: Heart failure monitoring with a single‑lead electrocardiogram at home

Yadav, A., Yadav, O., Gupta, N., & Gupta, R. (2025) · International Journal of Cardiology · doi:10.1016/j.ijcard.2025.133659 · Cited by 1 · see citing works

The Emerging Role of ECG-AI as a Digital Risk Biomarker for Incident Heart Failure

Oikonomou, E. (2026) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2025.11.039 · Cited by 1 · see citing works

Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational StudyPreprint

Pan, L., Li, S., Huo, J., Xiao, Z., Yu, Z., Chen, J., Zhou, Y., Li, Z., Zhang, B., Li, X., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6757316 · Cited by 1 · see citing works

Machine Learning in Cardio-Oncology: Innovation or Overhype?

Binder, C., Siegel, R. J., Cheng, P. P., Thadani, S. R., Ambrosy, A. P., & Ouyang, D. (2025) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-025-01129-8

Artificial Intelligence Electrocardiogram Risk Tool

Yadav, A., Samineni, L. N., & Gupta, R. (2025) · JAMA Cardiology · doi:10.1001/jamacardio.2025.4243

Electrocardiography Meets Artificial Intelligence: Shaping the Future of Heart Failure Prediction A Comprehensive ReviewPreprint

Blaiech, F. H., Hattay, J., & Machhout, M. (2026) · Research Square · doi:10.21203/rs.3.rs-8608402/v1

Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence–enabled electrocardiography

Lee, S. Y., Yoo, A.-H., Kang, S., Jang, J.-H., Jo, Y., Son, J. M., Lee, M. S., Han, G. I., Kwon, J., Lee, H. S., et al. (2026) · Journal of Cardiovascular Imaging · doi:10.1186/s44348-026-00066-9

Artificial intelligence in early heart failure detection: from algorithms to bedside integration

Zavaleta‐Monestel, E., Arguedas‐Chacón, S., & Speranza, M. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1717911

Contemporary Diagnosis of Heart Failure: Integrating Traditional Tools and Emerging Biomarkers

Kaur, J., Staroselsky, M., Berman, L., Kelschenbach, K., Oprysko, C., Materia, A., Makaryus, A. N., & Zeltser, R. (2026) · International Journal of Angiology · doi:10.1055/a-2846-3245

Diagnostic Performance of Artificial Intelligence-Driven Electrocardiogram Algorithms in Detecting Heart Failure with Preserved Ejection Fraction: A Systematic Review and Meta-Analysis

David, G. B., Ramos, N., Huntermann, R., de Oliveira, J. P., Molinari, M. E., & Bacca, C. D. O. F. (2026) · International Journal of Cardiovascular Sciences · doi:10.36660/ijcs.20250091

Training Trajectories Reveal Cross-Modality Generalization Gaps in 12-Lead–Trained AI-ECG Deployed on Smartwatch Recordings (Preprint)Preprint

Lee, H. S., Jang, J.-H., Kang, S., Jo, Y., Son, J. M., Lee, M. S., Kim, K. S., Kwon, J., & Kim, K. (2026) · doi:10.2196/preprints.100524

Emerging Artificial Intelligence Tools for the Screening of Structural and Valvular Heart Disease

Abbaoui, Y., Nolin-Lapalme, A., Morisset, J., Adib, I. E., Généreux, P., Poterucha, T. J., Elias, P. A., Yao, X., & Avram, R. (2026) · Current Heart Failure Reports · doi:10.1007/s11897-026-00757-w

Enhancing Signal‐to‐Noise Ratio in Flexible ECG Monitoring: Materials, Structural Designs, and Algorithmic Strategies

Sun, X., Zhang, C., Sun, G., Wang, P., Meng, C., & Li, Z. J. (2026) · Advanced Functional Materials · doi:10.1002/adfm.76294

AI in cardiovascular disease classification—a systematic literature review

Karandikar, M., Sawant, S., Kumbhar, S., & Bidwe, R. V. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11592-9

Mapping the research landscape of artificial intelligence in heart failure: a bibliometric analysis

Rashidian, P., Jala, S. R. A., Somu, K. P., Mahapatro, A., Chellapuram, N., Hashemi, S. M., Alotaibi, A. J., Hasan, O. H., Nasrollahizadeh, A., Letafatkar, N., et al. (2026) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000005235

Bridging the gap from clinical to home ECG: quantifying and overcoming accuracy loss in AI-enabled single-lead ECG models

Lin, C., Liu, W., Chen, K., Tsai, D., Chang, D.-W., Tsai, T.-N., Cheng, C.-C., Lin, C.-Y., Chen, Y.-H., Chien-Sung, T., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02919-5

DIGITAL TECHNOLOGIES IN HEART FAILURE CARE: ARTIFICIAL INTELLIGENCE, REMOTE MONITORING, AND SOCIAL IMPLICATIONS FOR EQUITABLE CARDIOVASCULAR HEALTH — A NARRATIVE REVIEW OF THE LITERATURE, 2022–2026

Marusza, M., Krupski, A., Stielow, V., & Zatyka, A. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.2(50).2026.5374

Artificial Intelligence in Cardiology: Applications in Diagnosis and Risk Prediction

Anand Sekar, G., Kulkarni, A. V., Sharma, C. K., Tank, B. Y., Sharat Vishwanath, K., & Lakhani, H. A. (2026) · Cureus · doi:10.7759/cureus.112841

From Detection to Differentiation: The Evolving Role of Artificial Intelligence–Enabled ECG in Heart Failure Care

Batinica, B., Pedroso, A. F., & Khera, R. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.046267

Innovative strategies to reduce heart failure hospitalizations: insights from the ESC heart failure congress 2026

Haichin, K., Leroux, J., Giannetti, N., & Sharma, A. (2026) · Heart Failure Reviews · doi:10.1007/s10741-026-10662-y

Recalibrating Risk: A Call for South Asian-Focused Heart Failure Prediction Models

Nair, N., Du, D., Pillai, A. J., Mahesh, S., Munagala, M. R., Eisen, H. J., & Mahesh, B. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15166181

Artificial intelligence–enabled electrocardiography for assessment of left ventricular systolic dysfunction in the era of foundation models

Bollmann, A., Pradler, V., Husser, D., Kim, I., Hohenstein, S., Nitsche, A., Kwast, S., Kannt, O., Pawlu, C., Moscho, A., et al. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1899224

ECG-based longitudinal risk prediction across diseases and organ systemsPreprint

ye, Y., Zeng, Z., Tian, X., Yuan, Z., Wang, J., & Zhu, Y. (2026) · medRxiv · doi:10.64898/2026.08.29.26361697

Reading between the waves: what does AI-ECG capture when estimating NT-proBNP?

Lee, D., Grittner, U., & Friebel, J. (2026) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcag141

Artificial intelligence to support clinical practice across the inpatient heart failure care pathway: a systematic review

Wolf, E. V., Grapp, M., Hartmann, M., Friederich, H., & Bugaj, T. J. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag151

The PREVENT equations evidence appraisal of validation and clinical implications

Mortada, I., Elkolaly, E. A., Lee, A. W., Woods, R., Qadeer, A., Albayyaa, M., Blackwell, T. A., & Jneid, H. M. (2026) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2026.103471

Advances in Artificial Intelligence-based Cardiovascular Disease Prevention and Assessment

Pacella, D., Simone, A. D., & Ordine, L. (2026) · Heart Failure Clinics · doi:10.1016/j.hfc.2026.08.005

Artificial Intelligence–Enabled Detection and Management of Physiological Instability in Heart Failure

Sideris, K., Schmalfuss, C. M., Lewis, N. P., Sallam, K., Bozkurt, B., Hanff, T. C., Young, M. T., Schofield, R., Kyriakopoulos, C. P., Hanson, H., et al. (2026) · JACC Heart Failure · doi:10.1016/j.jchf.2026.103422

Artificial intelligence in cardiovascular imaging and electrocardiography

Xiao, Z., Wang, S., & Liang, Y. (2026) · Cardiology Plus · doi:10.1097/cp9.0000000000000174

A Prediction Model for Heart Failure with Improved Ejection Fraction Phenotype Based on Electrocardiogram Images and Clinical Baseline Features

Wei, X., Xiao, X., 张树龙, Dong, C., Yan, H., Ma, Y., Yu, Q., & Liu, L. (2026) · International Journal of General Medicine · doi:10.2147/ijgm.s626451

Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational StudyPreprint

Dhingra, L. S., Aminorroaya, A., Sangha, V., Pedroso, A. F., Asselbergs, F. W., Brant, L. C. C., Barreto, S. M., Ribeiro, A. L. P., Krumholz, H. M., Oikonomou, E. K., et al. (2024) · medRxiv · doi:10.1101/2024.04.02.24305232 · Cited by 9 · see citing works

Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study

Dhingra, L. S., Aminorroaya, A., Sangha, V., Pedroso, A. F., Asselbergs, F. W., Brant, L. C. C., Barreto, S. M., Ribeiro, A. L. P., Krumholz, H. M., Oikonomou, E. K., et al. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehae914 · Cited by 64 · see citing works

Predicting Heart Failure From 12-Lead ECGs Using AI

Desai, A. S., Pandey, A., Suratekar, R., Ahmad, F. S., Alger, H. M., Anto, A. G., Nujum G, S., Lopez-Jimenez, F., Oh, J. K., Asfahan, S., et al. (2026) · JACC · doi:10.1016/j.jacc.2025.10.065 · Cited by 16 · see citing works

Deep learning analysis of single-lead electrocardiograms enables pragmatic heart failure risk assessment in the general population

Neyazi, M., Bremer, J. P., Brederecke, J., Knorr, M. S., Seum, F., Reinbold, C., Groß, S., Csengeri, D., Blankenberg, S., Dörr, M., et al. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag099

Multinational assessment of traditional and AI-electrocardiography based risk prediction for heart failure in prospective cohort studies

Pedroso, A. F., Dhingra, L., Aminorroaya, A., de Figueiredo, R. C., Brant, L. C. C., Barreto, S. M., Ribeiro, A. L. P., & Khera, R. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf784.1053