Artificial Intelligence in Cardiology

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362

The perceptron: A probabilistic model for information storage and organization in the brain.

Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280

Mastering the game of Go without human knowledge

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697

Deep learning for healthcare: review, opportunities and challenges

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076

Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks

Serkan Kiranyaz, B., İnce, T., & Gabbouj, M. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2468589 · Cited by 1,997

Predicting good probabilities with supervised learning

Niculescu-Mizil, A., & Caruana, R. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102430 · Cited by 1,884

Deep patient: An unsupervised representation to predict the future of patients from the electronic health records

Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710

An accurate and efficient method to train classifiers for atrial fibrillation detection in ECGs: Learning by asking better questions

Wesselius, F. J., van Schie, M. S., de Groot, N. M. S., & Hendriks, R. C. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105331 · Cited by 11

The Role of Artificial Intelligence in Predicting Outcomes by Cardiovascular Magnetic Resonance: A Comprehensive Systematic Review

Assadi, H. S., Alabed, S., Maiter, A., Salehi, M., Li, R., Ripley, D. P., van der Geest, R. J., Zhong, Y., Zhong, L., Swift, A. J., et al. (2022) · Medicina · doi:10.3390/medicina58081087 · Cited by 13

Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare – a proof of concept study for the prediction of coronary artery calcification scores

Wolff, J., Matschinske, J., Baumgart, D., Pytlik, A., Keck, A., Natarajan, A., von Schacky, C. E., Pauling, J. K., & Baumbach, J. (2022) · Journal of Integrative Bioinformatics · doi:10.1515/jib-2022-0032 · Cited by 11

The Prospect of Genomic, Transcriptomic, Epigenetic and Metabolomic Biomarkers for The Personalized Prevention of Type 2 Diabetes and Cardiovascular Diseases

Zeljković, A., Mihajlović, M., Vujčić, S., Guzonjić, A., Munjas, J., Stefanović, A., Kotur-Stevuljevic, J., Rizzo, M., Bogavac‐Stanojević, N., Gagić, J., et al. (2023) · Current Vascular Pharmacology · doi:10.2174/1570161121666230510141338 · Cited by 11

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

Rao, S. J., Iqbal, S. B., Isath, A. M., Virk, H. U. H., Wang, Z., Glicksberg, B. S., & Krittanawong, C. (2024) · Hearts · doi:10.3390/hearts5010007 · Cited by 12

Strategic Opportunities for Leveraging Low-cost, High-impact Technological Innovations to Promote Cardiovascular Health in India

Prabhakaran, D., Ajay, V. S., & Tandon, N. (2019) · Ethnicity & Disease · doi:10.18865//ed.29.s1.145 · Cited by 11

Wireless monitoring and artificial intelligence: A bright future in cardiothoracic surgery

Kalfa, D. M., Agrawal, S. K., Goldshtrom, N., LaPar, D. J., & BACHA, E. A. (2019) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2019.08.141 · Cited by 11

Research output of artificial intelligence in arrhythmia from 2004 to 2021: a bibliometric analysis

Huang, J., Liu, Y., Huang, S., Ke, G., Chen, X., Gong, B., Wei, W., Xue, Y., Deng, H., & Wu, S. (2022) · Journal of Thoracic Disease · doi:10.21037/jtd-21-1767 · Cited by 11

Federated learning (FL)-driven real-time decision support for intraoperative cardiovascular surgery: A privacy-preserving AI framework

Kanthavel, R., & Dhaya, R. (2025) · Innovation and Emerging Technologies · doi:10.1142/s2737599425500252 · Cited by 10

Research related to the diagnosis of prostate cancer based on machine learning medical images: A review

Chen, X., Liu, X., Wu, Y., Wang, Z., & Wang, S. H. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105279 · Cited by 11

Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality

Nazar, W., Nazar, K., & Daniłowicz‐Szymanowicz, L. (2024) · Life · doi:10.3390/life14060761 · Cited by 10

Diagnostic Strategies Using AI and ML in Cardiovascular Diseases: Challenges and Future Perspectives

Rana, N., Sharma, K., & Sharma, A. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-96-1285-7_7 · Cited by 11

Artificial Intelligence for sustainable logistics: Reducing carbon emissions and fuel consumption through route optimization

Sarioguz, O. (2025) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2025.15.3.1933 · Cited by 11

Machine Learning Methodologies for Prediction of Rhythm-Control Strategy in Patients Diagnosed With Atrial Fibrillation: Observational, Retrospective, Case-Control Study

Kim, R., Simon, S. T., Powers, B., Sandhu, A., Sánchez, J. M., Borne, R. T., Tumolo, A. Z., Zipse, M. M., West, J. J., Aleong, R. G., et al. (2021) · JMIR Medical Informatics · doi:10.2196/29225 · Cited by 10

Beyond prediction: Off‐target uses of artificial intelligence‐based predictive analytics in a learning health system

Keim‐Malpass, J., Moorman, L. P., Monfredi, O., Clark, M., & Bourque, J. M. (2022) · Learning Health Systems · doi:10.1002/lrh2.10323 · Cited by 10

Bibliometric analysis of 100 top cited articles of heart failure–associated diseases in combination with machine learning

Kuang, X., Zhong, Z., Liang, W., Huang, S., Luo, R., Luo, H., & Li, Y. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1158509 · Cited by 11

A Novel Light U-Net Model for Left Ventricle Segmentation Using MRI

Irshad, M., Yasmin, M., Sharif, M. I., Rashid, M., Sharif, M. I., & Kadry, S. (2023) · Mathematics · doi:10.3390/math11143245 · Cited by 10

The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot

Jacquemyn, X., Kutty, S., & Manlhiot, C. (2023) · CJC Pediatric and Congenital Heart Disease · doi:10.1016/j.cjcpc.2023.08.005 · Cited by 10

The LMIT: Light-mediated minimally-invasive theranostics in oncology

Fan, Y., Liu, S., Gao, E., Guo, R., Dong, G., Li, Y., Gao, T., Tang, X., & Liao, H. (2023) · Theranostics · doi:10.7150/thno.87783 · Cited by 10

Explainable Machine Learning and Online Calculators to Predict Heart Failure Mortality in Intensive Care Units

Chen, A., Zhang, Y., & Zhang, J. (2024) · ESC Heart Failure · doi:10.1002/ehf2.15062 · Cited by 10

What will we ask to artificial intelligence for cardiovascular medicine in the next decade?

Gallone, G., BRUNO, F., D’ASCENZO, F., & FERRARI, G. M. D. (2021) · Minerva Cardiology and Angiology · doi:10.23736/s2724-5683.21.05753-7 · Cited by 10

Cardiovascular Disease Prognosis and Analysis Using Machine Learning Techniques

Kapoor, A., Kapoor, S., Upreti, K., Singh, P., Kapoor, S., Alam, M. S., & Nasir, M. S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-25088-0_15 · Cited by 10

A Review of the Scope, Future, and Effectiveness of Using Artificial Intelligence in Cardiac Rehabilitation: A Call to Action for the Kingdom of Saudi Arabia

Alqahtani, A. S. (2023) · Applied Artificial Intelligence · doi:10.1080/08839514.2023.2175111 · Cited by 10

Phenotyping of risk factors and prediction of inhospital mortality in patients with coronary artery disease after coronary artery bypass grafting based on explainable artificial intelligence methods

Geltser, B. I., Shakhgeldyan, K. I., Rublev, V. Y., Domzhalov, I. G., Михайлович, Ц. М., & Shekunova, O. I. (2023) · Russian Journal of Cardiology · doi:10.15829/1560-4071-2023-5302 · Cited by 9

Machine Learning in Invasive and Noninvasive Coronary Angiography

Ünlü, O., & Fahed, A. C. (2023) · Current Atherosclerosis Reports · doi:10.1007/s11883-023-01178-z · Cited by 9

Artificial Intelligence–Derived Risk Prediction: A Novel Risk Calculator Using Office and Ambulatory Blood Pressure

Guimarães, P., Keller, A., Böhm, M., Lauder, L., Fehlmann, T., Ruilope, L. M., Vinyoles, E., Gorostidi, M., Segura, J., Ruiz‐Hurtado, G., et al. (2024) · Hypertension · doi:10.1161/hypertensionaha.123.22529 · Cited by 9

Machine Learning Models With Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: Multicenter Cohort Study

Dong, J., Jin, Z., Li, C., Yang, J., Jiang, Y., Li, Z., Chen, C., Zhang, B., Ye, Z., Hu, Y., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/68509 · Cited by 10

Clinical Applications of Artificial Intelligence in Vascular Surgery

Joh, J. H. (2025) · Vascular Specialist International · doi:10.5758/vsi.240120 · Cited by 9

Artificial Intelligence Models in Diagnosis and Treatment of Kidney Diseases: Current Status and Prospects

Li, C., Liu, J., Fu, P., & Zou, J. (2025) · Kidney Diseases · doi:10.1159/000546397 · Cited by 9

Artificial intelligence in interventional cardiology: a review of its role in diagnosis, decision-making, and procedural precision

Nzeako, T. R., Elendu, C., Echefu, G. C., Olanisa, O., Kiladejo, A., & Bob-Manuel, E. D. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000003602 · Cited by 9

Advancing cancer care through artificial intelligence: from innovative models to clinical decision-making and regulatory integration

Subhan, A., & Manoharan, G. (2025) · Clinical Cancer Bulletin · doi:10.1007/s44272-025-00052-0 · Cited by 9

Multimodal Transformer–Based Electrocardiogram Analysis for Cardiovascular Comorbidity Detection: Model Development and Validation Study

Yang, Z., Wang, X., Wang, J., Guang, Q., Ding, X., Liu, H., Xu, Y., Zhao, J., & Bai, M. (2025) · JMIR Formative Research · doi:10.2196/80815 · Cited by 9

Future proofing expertise: AI as a power-up, not a takeover

Toscani, G. (2025) · Futures · doi:10.1016/j.futures.2025.103728 · Cited by 9

Machine Learning for Chronic Disease Predictive Analysis for Early Intervention and Personalized Care

Mondal, R. S., Bhuiyan, M. N. A., & Akter, L. (2024) · Applied IT & Engineering · doi:10.25163/engineering.2110301 · Cited by 9

Cardiologists and the Cardiology of the Future. Vision and proposals of the Spanish Society of Cardiology for the Cardiology of the 21st Century

Sánchez, M. A., Martín, J. J. A., Cequier, A. R., Gómez‐Doblas, J. J., Rivera, L. P., Goya, I. L., Rodrigo, F. R., Íñiguez, A., Miguel, C. M., Evangelista, A., et al. (2019) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2019.02.025 · Cited by 9

Endotyping in Heart Failure ― Identifying Mechanistically Meaningful Subtypes of Disease ―

Liang, L. W., & Shimada, Y. J. (2021) · Circulation Journal · doi:10.1253/circj.cj-21-0349 · Cited by 9

Assessment of the Impact of Alcohol Consumption Patterns on Heart Rate Variability by Machine Learning in Healthy Young Adults

Pop, G. N., Christodorescu, R., Velimirovici, D. E., Șoșdean, R., Corbu, M., Bodea, O., Valcovici, M. D., & Dragan, S. R. (2021) · Medicina · doi:10.3390/medicina57090956 · Cited by 9

Artificial Intelligence and Mechanical Circulatory Support

Li, S., Hickey, G., Lander, M. M., & Kanwar, M. K. (2022) · Heart Failure Clinics · doi:10.1016/j.hfc.2021.11.005 · Cited by 9

Development and Validation of a Machine Learning Score for Readmissions After Transcatheter Aortic Valve Implantation

Sulaiman, S., Kawsara, A., Mahayni, A. A., Sabbagh, A. E., Singh, M., Crestanello, J. A., Gulati, R., & Alkhouli, M. A. (2022) · JACC Advances · doi:10.1016/j.jacadv.2022.100060 · Cited by 9

Impact of artificial intelligence on healthcare

Francis, J., Varghese, J. V., & Thomas, A. (2023) · International Journal of Advances in Medicine · doi:10.18203/2349-3933.ijam20232839 · Cited by 9

AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment

Rodriguez-Rodriguez, A. M., la Fuente-Costa, M. D., la Riva, M. E.-D., Dominguez, B. P., Ares, G. P., Casaña, J., & Blanco‐Díaz, M. (2024) · SSM - Population Health · doi:10.1016/j.ssmph.2024.101677 · Cited by 9

A Neuronal Network-Based Score Predicting Survival in Patients Undergoing Aortic Valve Intervention: The ABC-AS Score

Barbieri, F., Pfeifer, B. E., Senoner, T., Dobner, S., Spitaler, P., Semsroth, S., Lambert, T., Zweiker, D., Neururer, S. B., Scherr, D., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13133691 · Cited by 9

The Future of Healthcare: Using AI and IoT to Drive Data Driven Revolution

Venkateswarlu, V. T., Manimozhi, P., Janani, R., Subbarao, K. V., Tamilselvan, S., & Kumar, G. G. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-65022-2_9 · Cited by 9

Artificial intelligence driven plaque characterization and functional assessment from CCTA using OCT-based automation: A prospective study

Han, J., Wang, Z., Chen, T., Liu, S., Tan, J., Sun, Y., Feng, L., Zhang, D., Ma, L., Liu, H., et al. (2025) · International Journal of Cardiology · doi:10.1016/j.ijcard.2025.133140 · Cited by 9

Predicting survival in heart failure: a risk score based on machine-learning and change point algorithm

Kim, W., Park, J. J., Lee, H., Kim, K. H., Yoo, B., Kang, S., Baek, S. H., Jeon, E., Kim, J., Cho, M., et al. (2021) · Clinical Research in Cardiology · doi:10.1007/s00392-021-01870-7 · Cited by 9

Supervised Learning Based Classification of Cardiovascular Diseases

Hussain, A., Malik, H., & Chaudhry, M. U. (2021) · Proceedings of Engineering and Technology Innovation · doi:10.46604/peti.2021.7217 · Cited by 9

Zwischen Automatisierung und ethischem Anspruch – Disruptive Effekte des KI-Einsatzes in und auf Professionen der Gesundheitsversorgung

Schneider, D., Sonar, A., & Weber, K. (2022) · Springer eBooks · doi:10.1007/978-3-658-33597-7_14 · Cited by 9

Electrocardiographic, echocardiographic and lipid parameters in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome

Михайлович, Ц. М., Geltser, B. I., Shakhgeldyan, K. I., Дмитриевна, Е. Е., Zavalin, G. S., & Shekunova, O. I. (2022) · Russian Journal of Cardiology · doi:10.15829/1560-4071-2022-5036 · Cited by 9

Una nueva estrategia para las cardiopatías congénitas del adulto: la inteligencia artificial

Orwat, S., Arvanitaki, A., & Diller, G. (2021) · Revista Española de Cardiología · doi:10.1016/j.recesp.2020.12.009 · Cited by 9

Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients’ electrocardiograms

Alahdab, F., Saad, M. B., Ahmed, A. I., Al-Tashi, Q., Aminu, M., Han, Y., Moody, J. B., Murthy, V. L., Wu, J., & Al-Mallah, M. H. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101746 · Cited by 10

Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels

Jin, H., Zhang, L., Sun, Y., Xu, Y., & Luo, M. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1582779 · Cited by 8

Integrating Artificial Intelligence Into Digital Health-Optimized Therapeutics for the Remote Cardiac Rehabilitation

Wan, X., Ge, L., Tian, J., Ding, G., Gao, B., Geng, H., Ge, C., & Song, X. (2026) · Clinical Medicine Insights Cardiology · doi:10.1177/11795468261421344 · Cited by 8

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

Methods of Using Fuzzy Interval Logic During Processing of Space States of Complex Biophysical Objects

Lyashenko, V. V. (2020) · International Journal of Emerging Trends in Engineering Research · doi:10.30534/ijeter/2020/22822020 · Cited by 8

Combining digital data and artificial intelligence for cardiovascular health

Soto, J. T., Hershman, S. G., & Ashley, E. A. (2021) · Cardiovascular Research · doi:10.1093/cvr/cvab211 · Cited by 8

Machine learning algorithm can provide assistance for the diagnosis of non-ST-segment elevation myocardial infarction

Qin, L., Qi, Q., Ainiwaer, A., Hou, W., Zuo, C. X., & Ma, X. (2022) · Postgraduate Medical Journal · doi:10.1136/postgradmedj-2021-141329 · Cited by 8

Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature

Lakkimsetti, M., Devella, S. G., Patel, K. B., Dhandibhotla, S., Kaur, J., Mathew, M., Kataria, J., Nallani, M., Farwa, U. E., Patel, T. D., et al. (2024) · Cureus · doi:10.7759/cureus.58400 · Cited by 8

Broadening Perspectives of Artificial Intelligence in Echocardiography

Seetharam, K., Thyagaturu, H. S., Ferreira, G. L., Patel, A., Patel, C. P., Elahi, A., PACHULSKI, R. T., Shah, J. M., Mir, P., Thodimela, A., et al. (2024) · Cardiology and Therapy · doi:10.1007/s40119-024-00368-3 · Cited by 9

Application of Artificial Intelligence in Cardiology: A Bibliometric Analysis

Sidik, A. I., Komarov, R. N., Gawusu, S., Moomin, A., Al-Ariki, M. K., Elias, M., Sobolev, D., Karpenko, I. G., Esion, G., Akambase, J., et al. (2024) · Cureus · doi:10.7759/cureus.66925 · Cited by 9

Artificial Intelligent in Healthcare

Meiliana, A., Dewi, N. M., & Wijaya, A. (2019) · The Indonesian Biomedical Journal · doi:10.18585/inabj.v11i2.844 · Cited by 9

Intelligence artificielle et téléradiologie en imagerie cardiaque en coupe

Pasteur‐Rousseau, A., & Paul, J. (2021) · Annales de Cardiologie et d Angéiologie · doi:10.1016/j.ancard.2021.08.001 · Cited by 8

A Machine Learning-Based Prediction Model for the Extremely Rapid Decline of eGFR in Patients with Chronic Kidney Disease Using a Big Database

Inaguma, D., Hayashi, H., Yanagiya, R., Koseki, A., Iwamori, T., Kudo, M., Fukuma, S., & Yuzawa, Y. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-058833 · Cited by 8

A novel 6-metabolite signature for prediction of clinical outcomes in type 2 diabetic patients undergoing percutaneous coronary intervention

Wang, X., Cui, N., & Liu, X. (2022) · Cardiovascular Diabetology · doi:10.1186/s12933-022-01561-1 · Cited by 8

The scientific progress and prospects of artificial intelligence in digestive endoscopy: A comprehensive bibliometric analysis

Gan, P., Huang, S., Pan, X., Tan, S., Zhong, C., Zhou, X., Tang, X., Zhou, X., & Tang, X. (2022) · Medicine · doi:10.1097/md.0000000000031931 · Cited by 8

Multimodality Imaging in Sarcomeric Hypertrophic Cardiomyopathy: Get It Right…on Time

Galluzzo, A., Fiorelli, F., Rossi, V. A., Monzo, L., Montrasio, G., Camilli, M., Hálasz, G., Uccello, G., Mollace, R., & Beltrami, M. (2023) · Life · doi:10.3390/life13010171 · Cited by 8

Machine Learning in Modern Healthcare

Arora, A., & Basu, N. (2023) · International Journal of Advanced Medical Sciences and Technology · doi:10.54105/ijamst.d3037.063423 · Cited by 8

Machine Learning in Cardiac Imaging: Exploring the Art of Cluster Analysis

Namasivayam, M. J. (2021) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2021.05.011 · Cited by 8

Machine Learning Computer Vision Point of Care Decision Support of Echocardiographic Identification of Hypertrophic Cardiomyopathy

Vemulapalli, S., Alenezi, F. A. M., Jeong, H. K., Giczewska, A., Chiswell, K., Douglas, P. S., Zhang, A. R., Shah, S. H., Henao, R., & Wang, A. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.101746 · Cited by 7

TECH-DRIVEN TRUST: THE ROLE OF AI AND EMERGING TECHNOLOGIES IN PHARMACEUTICAL QUALITY ASSURANCE

KRISHNAN, P., Krishnan, N. J. S., DEY, A., SIVAKUMAR, S., RAVICHANDRAN, S., & BHARATHI, M. (2025) · International Journal of Applied Pharmaceutics · doi:10.22159/ijap.2025v17i5.54474 · Cited by 7

Unsupervised Machine Learning for Cardiovascular Disease: A Framework for Future Studies

Bresso, E., Lacomblez, C., Duarte, K., Monzo, L., Baudry, G., Tromp, J., Sharma, A., & Girerd, N. (2025) · European Journal of Heart Failure · doi:10.1002/ejhf.70076 · Cited by 7

Randomized‐Grid Search for Time‐Efficient Hyperparameter Tuning in Cardiovascular Disease Classification

Pathak, A. K., Chaubey, M., & Gupta, M. (2026) · Advanced Theory and Simulations · doi:10.1002/adts.70558 · Cited by 9

The clinical value of dual-energy CT imaging in preoperative evaluation of pathological types of gastric cancer

He, Y., Qi, X., Luo, X., Wang, W., Yang, H., Xu, M. Q., Wu, X., & Fan, W. (2023) · Technology and Health Care · doi:10.3233/thc-220664 · Cited by 7

Expectations of Anesthesiology and Intensive Care Professionals Toward Artificial Intelligence: Observational Study

Kloka, J. A., Holtmann, S. C., Nürenberg‐Goloub, E., Piekarski, F., Zacharowski, K. D., & Friedrichson, B. (2023) · JMIR Formative Research · doi:10.2196/43896 · Cited by 7

Establishment and validation of an AI-aid method in the diagnosis of myocardial perfusion imaging

Zhang, R., Wang, P., 边艳珠, Fan, Y., Li, J., Liu, X., Shen, J., Hu, Y., Liao, X., Wang, H., et al. (2023) · BMC Medical Imaging · doi:10.1186/s12880-023-01037-y · Cited by 7

Development and validation of machine learning algorithms to predict posthypertensive origin in left ventricular hypertrophy

Beneyto, M. M., Ghyaza, G., Cariou, E., Amar, J., & Lairez, O. (2023) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2023.06.005 · Cited by 7

Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models

Mamas, M. A., Roffi, M., Fröbert, O., Chieffo, A., Beneduce, A., Matetić, A., Tonino, P. A. L., Paunovic, D., Jacobs, L., Debrus, R., et al. (2023) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztad051 · Cited by 7

Elements of artificial intelligence in a predictive personalized model of pharmacotherapy choice in patients with heart failure with mildly reduced ejection fraction of ischemic origin

Osipova, O. A., Васильевна, К. А., Demko, V. V., Гостева, Е. В., Komisov, A. A., Kuzub, А. A., Викторовна, С. А., Brizhaneva, A. S., Шепель, Р. Н., & Drapkina, O. M. (2023) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2023-3619 · Cited by 7

Emerging technologies and applications in multimodality imaging for ischemic heart disease: current state and future of artificial intelligence

Sekar, P. K. C., & Veerabathiran, R. (2024) · Exploration of Cardiology · doi:10.37349/ec.2024.00038 · Cited by 7

Identifying Key Predictors of Students’ Competency Achievement Using Machine Learning Models: A Bioengineering Case Study

Quintana-Zaez, J. C., Vázquez‐Villegas, P., & Valdés-Ramírez, D. (2025) · IEEE Access · doi:10.1109/access.2025.3613250 · Cited by 7

A new approach to modelling in adult congenital heart disease: artificial intelligence

Orwat, S., Arvanitaki, A., & Diller, G. (2021) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2020.12.008 · Cited by 7

Artificial intelligence in clinical decision-making for diagnosis of cardiovascular disease using epigenetics mechanisms

Karađuzović-Hadžiabdić, K., & Peters, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822258-4.00020-1 · Cited by 7

Use of Neural Network model to examine post-operative infections following orthognathic surgeries in the United States

Allareddy, V., Lee, M. K., Vaid, N. R., & Yadav, S. (2021) · Seminars in Orthodontics · doi:10.1053/j.sodo.2021.05.009 · Cited by 7

Implementing an Artificial Intelligence System in the Work of General Practitioner in the Yamalo-Nenets Autonomous Okrug: Pilot Cross-sectional Screening Observational Study

Васильевна, Ж. Е., & Rubtsova, E. V. (2022) · Кубанский научный медицинский вестник · doi:10.25207/1608-6228-2022-29-4-14-31 · Cited by 7

Artificial Intelligence for the Future of Medicine

Ruiz, R. B., & Velásquez, J. D. (2022) · Intelligent systems reference library · doi:10.1007/978-3-031-11170-9_1 · Cited by 8

Therapeutic components of digital counseling for chronic heart failure

Fezza, G. C., Sansone, S., & Nolan, R. P. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.888524 · Cited by 7

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

McDonnell, K. J. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12144830 · Cited by 7

Identifying Premature Ventricular Complexes from Outflow Tracts Based on PVC Configuration: A Machine Learning Approach

Bajaj, S., Bennett, M. T., & Rabkin, S. W. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12175558 · Cited by 7

Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review

Puterman‐Salzman, L., Katz, J., Bergman, H., Grad, R. M., Khanassov, V., Gore, G. C., Vedel, I., Wilchesky, M., Armanfard, N., Ghourchian, N., et al. (2023) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000533693 · Cited by 7

Identification of Potential Targets of Stress Cardiomyopathy by a Machine Learning Algorithm

Jin, X., Ji, X., Yin, H., Zhang, J., Lin, P., Pei, Q., Su, D., Li, B., Qu, X., Yin, D., et al. (2024) · Cardiovascular Innovations and Applications · doi:10.15212/cvia.2024.0011 · Cited by 7

Comparison of machine learning tools for the prediction of AMD based on genetic, age, and diabetes-related variables in the Chinese population

Hao, S., Bai, J., Liu, H., Wang, L., Liu, T., Lin, C., Luo, X., Gao, J., Zhao, J., Li, H., et al. (2020) · Regenerative Therapy · doi:10.1016/j.reth.2020.09.001 · Cited by 7

Addressing signal alterations induced in CT images by deep learning processing: A preliminary phantom study

Doria, S., Valeri, F., Lasagni, L., Sanguineti, V., Ragonesi, R., Akbar, M. U., Gnerucci, A., Bue, A. D., MARCONI, A., Risaliti, G., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.022 · Cited by 7

The Digital Future of Heart Failure Care

Cowie, M., McBeath, K., & Angermann, C. E. (2022) · Current Heart Failure Reports · doi:10.1007/s11897-022-00547-0 · Cited by 7

Blockchain Security for Artificial Intelligence-Based Clinical Decision Support Tool

Vijayalakshmi, S., SAVITA, S., Gayathri, S. P., & Janarthanan, S. (2021) · Springer eBooks · doi:10.1007/978-3-030-74150-1_10 · Cited by 7

Cardiology in the Digital Era: From Artificial Intelligence to Metaverse, Paving the Way for Future Advancements

Skalidis, I., Kachrimanidis, I., Koliastasis, L., Arangalage, D., Antiochos, P., Maurizi, N., Müller, O., Fournier, S., Hamilos, M. I., & Skalidis, E. I. (2023) · Future Cardiology · doi:10.2217/fca-2023-0106 · Cited by 7

Machine learning score to predict in-hospital outcomes in patients hospitalized in cardiac intensive care unit

Weizman, O., Hamzi, K., Henry, P., Schurtz, G., Hauguel‐Moreau, M., Trimaille, A., Bédossa, M., Dib, J.-C., Attou, S., Boukertouta, T., et al. (2024) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae098 · Cited by 7

Application of artificial intelligence technologies in cardiovascular disease detection and management authors

Кутелев, Г. Г., Parfenov, S. A., Sapozhnikov, K. V., Lazarev, A. A., Kuzin, A. A., Glushakov, R. I., & Samokhin, S. О. (2025) · Translational Medicine · doi:10.18705/2311-4495-2024-11-6-562-576 · Cited by 6

Machine learning in modern cardiology

Alekseev, I., & Алексеев, И. А. (2020) · Electronic scientific archive of UrFU (Ural Federal University)

Using of machine learning methods in cardiology

Фирюлина, М. А., & Каширина, И. Л. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1902/1/012135 · Cited by 3

Машинное обучение в современной кардиологии

Alekseev, I. Y., & Алексеев, И. В. (2020) · Electronic Archive of Ural Federal University (ELAR UrFU)

ARTIFICIAL INTELLIGENCE IN PERSONALIZED CARDIOLOGY: CURRENT APPLICATIONS, CLINICAL IMPACT, AND FUTURE DIRECTIONS: A REVIEW

Ms. Nashra Usmani*1, M. S. F. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.18949016

Artificial intelligence in cardiovascular medicine

Ranka, S., Reddy, M., & Noheria, A. (2020) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000812 · Cited by 51