Artificial Intelligence in Cardiology

Deep learning

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Human-level control through deep reinforcement learning

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Dermatologist-level classification of skin cancer with deep neural networks

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

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

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Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Madanu, R., Abbod, M., Hsiao, F., Chen, W., & Shieh, J.-S. (2022) · Technologies · doi:10.3390/technologies10030074 · Cited by 28

Most Promising Therapies in Interventional Cardiology

Kernéis, M., Nafee, T., Yee, M. K., Kazmi, H. A., Datta, S., Zeitouni, M., Afzal, M. K., Jafarizade, M., Walia, S. S., Qamar, I., et al. (2019) · Current Cardiology Reports · doi:10.1007/s11886-019-1108-x · Cited by 28

A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring

Maurya, M. R., Riyaz, N. S., Reddy, M. S. B., Yalcin, H. C., Ouakad, H. M., Bahadur, I. M., Al-Maadeed, S. A., & Sadasivuni, K. K. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02447-2 · Cited by 28

Echocardiographic Detection of Regional Wall Motion Abnormalities Using Artificial Intelligence Compared to Human Readers

Slivnick, J. A., Gessert, N., Cotella, J. I., Oliveira, L., Pezzotti, N., Eslami, P., Sadeghi, A., Wehle, S., Prabhu, D. S., Waechter, I., et al. (2024) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2024.03.017 · Cited by 26

The Auxiliary Role of Artificial Intelligence Applications in Mitigating the Linguistic, Psychological, and Educational Challenges of Teaching and Learning Chinese Language by non-Chinese Students

Xia, J., Ge, Y., Shen, Z., & Najar, M. R. (2024) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v25i3.7680 · Cited by 32

A new era in colorectal cancer: Artificial Intelligence at the forefront

Farhoudian, A., Heidari, A., & Shahhosseini, R. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110926 · Cited by 27

Machine Learning in Cardiology—Ensuring Clinical Impact Lives Up to the Hype

Russak, A. J., Chaudhry, F., Freitas, J. K. D., Baron, G., Chaudhry, F. F., Bienstock, S., Paranjpe, I., Vaid, A., Ali, M., Zhao, S., et al. (2020) · Journal of Cardiovascular Pharmacology and Therapeutics · doi:10.1177/1074248420928651 · Cited by 27

Effect of Optimized Versus Guidelines‐Based Automated External Defibrillator Placement on Out‐of‐Hospital Cardiac Arrest Coverage: An In Silico Trial

Sun, C. L. F., Karlsson, L., Morrison, L. J., Brooks, S. C., Folke, F., & Chan, T. C. Y. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.016701 · Cited by 26

Machine learning-based long-term outcome prediction in patients undergoing percutaneous coronary intervention

Liu, S., Yang, S., Xing, A., Zheng, L., Shen, L., Tu, B., & Yao, Y. (2021) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt-21-37 · Cited by 26

Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease

Chen, G., & Shen, J. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.635764 · Cited by 26

Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence

Kwon, O., Hong, M., Kim, T., Hwang, I., Shim, J., Choi, E., Lim, H. E., Yu, H. T., Uhm, J., Joung, B., et al. (2022) · Open Heart · doi:10.1136/openhrt-2021-001898 · Cited by 26

Artificial intelligence in peritoneal dialysis: general overview

Bai, Q., & Tang, W. (2022) · Renal Failure · doi:10.1080/0886022x.2022.2064304 · Cited by 27

Explainable Deep Attention Active Learning for Sentimental Analytics of Mental Disorder

Ahmed, U., Jhaveri, R. H., Srivastava, G., & Lin, J. C. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3551890 · Cited by 26

Long-term single-lead electrocardiogram monitoring to detect new-onset postoperative atrial fibrillation in patients after cardiac surgery

He, K., Liang, W., Liu, S., Bian, L., Xu, Y., Luo, C., Li, Y., Yue, H., Yang, C., & Wu, Z. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1001883 · Cited by 28

Application of 4D printing and AI to cardiovascular devices

Faruque, M. O., Lee, Y., Wyckoff, G. J., & Lee, C. H. (2023) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2023.104162 · Cited by 27

Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: A machine learning-based prediction model by using a big database

Inaguma, D., Kitagawa, A., Yanagiya, R., Koseki, A., Iwamori, T., Kudo, M., & Yuzawa, Y. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239262 · Cited by 27

Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer’s Disease as a Case Study

Javed, R., Abbas, T., Shahzad, T., Kanwal, K., Ramay, S. A., Khan, M. A., & Ouahada, K. (2025) · IEEE Access · doi:10.1109/access.2025.3525514 · Cited by 25

An interpretable LightGBM model for predicting coronary heart disease: Enhancing clinical decision-making with machine learning

Deng, L., Lu, K., & Hu, H. (2025) · PLoS ONE · doi:10.1371/journal.pone.0330377 · Cited by 26

Cardiac arrest: prediction models in the early phase of hospitalization

Dumas, F., Bougouin, W., & Cariou, A. (2019) · Current Opinion in Critical Care · doi:10.1097/mcc.0000000000000613 · Cited by 25

A Machine Learning Tool to Predict the Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Cervical Cancer

Arezzo, F., Forgia, D. L., Venerito, V., Moschetta, M., Tagliafico, A. S., Lombardi, C. M., Loizzi, V., Cicinelli, E., & Cormio, G. (2021) · Applied Sciences · doi:10.3390/app11020823 · Cited by 25

Survey of Heart Disease Prediction and Identification using Machine Learning Approaches

Franklin, R. G., & Muthukumar, B. (2020) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iciss49785.2020.9316119 · Cited by 25

Machine Learning-Augmented Propensity Score Analysis of Percutaneous Coronary Intervention in Over 30 Million Cancer and Non-cancer Patients

Monlezun, D. J., Lawless, S. M., Palaskas, N. L., Peerbhai, S., Charitakis, K., Marmagkiolis, K., Lopez‐Mattei, J., Mamas, M. A., & Iliescu, C. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.620857 · Cited by 25

Deep Learning in the Management of Intracranial Aneurysms and Cerebrovascular Diseases: A Review of the Current Literature

Mensah, E. O., Pringle, C., Roberts, G., Gurusinghe, N. T., Golash, A., & Alalade, A. F. (2022) · World Neurosurgery · doi:10.1016/j.wneu.2022.02.006 · Cited by 26

Metabolomics and a Breath Sensor Identify Acetone as a Biomarker for Heart Failure

Gladding, P. A., Young, R., Cooper, M., Loader, S., Smith, K. K., Zarate, E., Green, S., Villas-Bôas, S. G., Shepherd, P., Kakadia, P. M., et al. (2022) · Biomolecules · doi:10.3390/biom13010013 · Cited by 25

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Medhi, D., Kamidi, S. R., Sree, K. P. M., Shaikh, S., Rasheed, S., Murichathil, A. H. T., & Nazir, Z. (2024) · Cureus · doi:10.7759/cureus.59661 · Cited by 27

Artificial intelligence: the future for cardiology

Miyazawa, A. A. (2019) · Heart · doi:10.1136/heartjnl-2018-314464 · Cited by 25

Statistical tools used for analyses of frequent users of emergency department: a scoping review

Chiu, Y. M., Racine-Hemmings, F., Dufour, I., Vanasse, A., Chouinard, M., Bisson, M., & Hudon, C. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-027750 · Cited by 25

Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives

Yang, Y., Liu, Y., Chen, Y., Luo, D., Xu, K., & Zhang, L. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1477130 · Cited by 28

Artificial intelligence in pediatric cardiology and cardiac surgery: Irrational hype or paradigm shift?

Chang, A. C. (2019) · Annals of Pediatric Cardiology · doi:10.4103/apc.apc_55_19 · Cited by 24

Derivation and validation of a computable phenotype for acute decompensated heart failure in hospitalized patients

Kashyap, R., Sarvottam, K., Wilson, G. A., Jentzer, J. C., Seisa, M. O., & KASHANI, K. B. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1092-5 · Cited by 24

Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis

Ferro, S., Bottigliengo, D., Gregori, D., Fabricio, A. S. C., Gion, M., & Baldi, I. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11040272 · Cited by 24

Prediction of Genotype Positivity in Patients With Hypertrophic Cardiomyopathy Using Machine Learning

Liang, L. W., Fifer, M. A., Hasegawa, K., Maurer, M. S., Reilly, M. P., & Shimada, Y. J. (2021) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.120.003259 · Cited by 25

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Carpenter, H. J., Ghayesh, M. H., Zander, A. C., Giovanni, G. D., Li, J., & Psaltis, P. J. (2022) · Tomography · doi:10.3390/tomography8030108 · Cited by 24

Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis

Zhang, J., Zhang, J., Jin, J., Jiang, X., Yang, L., Fan, S., Zhang, Q., & Chi, M. (2024) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2024.1323918 · Cited by 24

The future of bone regeneration: Artificial intelligence in biomaterials discovery

Fan, J., Xu, J., Wen, X., Sun, L. G., Xiu, Y., Zhang, Z., Liu, T., Zhang, D., Wang, P., & Xing, D. (2024) · Materials Today Communications · doi:10.1016/j.mtcomm.2024.109982 · Cited by 26

An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records

Diao, X., Huo, Y., Yan, Z., Wang, H., Yuan, J., Wang, Y., Cai, J., & Zhao, W. (2020) · JMIR Medical Informatics · doi:10.2196/19739 · Cited by 24

Yentl's Bikini: Sex Differences in STEMI

Gulati, M. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.012873 · Cited by 24

The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

Alkhodari, M., Xiong, Z., Khandoker, A. H., Hadjileontiadis, L. J., Leeson, P., & Lapidaire, W. (2023) · Expert Review of Cardiovascular Therapy · doi:10.1080/14779072.2023.2223978 · Cited by 23

A New Perspective on Precision Medicine: The Power of Digital Organoids

Yang, Q., Li, M., Xiao, Z., Feng, Y., Lei, L., & Li, S. (2025) · Biomaterials Research · doi:10.34133/bmr.0171 · Cited by 23

Artificial Intelligence in Cardiovascular Medicine: Historical Overview, Current Status, and Future Directions

Krajcer, Z. (2022) · Texas Heart Institute Journal · doi:10.14503/thij-20-7527 · Cited by 24

Subtle QRS changes are associated with reduced ejection fraction, diastolic dysfunction, and heart failure development and therapy responsiveness: Applications for artificial intelligence to ECG

García‐Escobar, A., Vera, S., y, A. J.-R., Jiménez‐Valero, S., Galeote, G., & Moreno, R. (2022) · Annals of Noninvasive Electrocardiology · doi:10.1111/anec.12998 · Cited by 23

Development of a Machine Learning Model to Predict Outcomes and Cost After Cardiac Surgery

Zea-Vera, R., Ryan, C. T., Navarro, S. M., Havelka, J., Wall, M. J., Coselli, J. S., Rosengart, T. K., Chatterjee, S., & Ghanta, R. K. (2022) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2022.06.055 · Cited by 23

Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score

Monlezun, D. J., Sinyavskiy, O., Peters, N., Steigner, L., Aksamit, T. R., Girault, M. I., Gómez, A. G., Gallagher, C. M., & Iliescu, C. (2022) · Medicina · doi:10.3390/medicina58081039 · Cited by 24

Comparing Conventional Statistical Models and Machine Learning in a Small Cohort of South African Cardiac Patients

Premsagar, P., Aldous, C. M., Esterhuizen, T. M., Gomes, B. J., Gaskell, J. W., & Tabb, D. L. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101103 · Cited by 23

Creeping Fat in Crohn’s Disease—Surgical, Histological, and Radiological Approaches

Aggeletopoulou, I., Tsounis, E. P., Mouzaki, Α., & Triantos, C. K. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13071029 · Cited by 25

Big Data Approaches in Heart Failure Research

Lanzer, J. D., Leuschner, F., Kramann, R., Levinson, R. T., & Sáez-Rodríguez, J. (2020) · Current Heart Failure Reports · doi:10.1007/s11897-020-00469-9 · Cited by 23

Big Data–Artificial Intelligence Fusion Technology in Education in the Context of the New Crown Epidemic

Zhao, J., & Li, Q. (2022) · Big Data · doi:10.1089/big.2021.0245 · Cited by 23

Leading-edge Artificial Intelligence (AI) and Internet of Things (IoT) Technologies for Enhanced Geotechnical Site CharacterizationPreprint

Rane, N. L., Choudhary, S., & Rane, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4640926 · Cited by 23

Artificial intelligence for education, proctoring, and credentialing in cardiovascular medicine

Krajcer, Z. (2022) · Texas Heart Institute Journal · doi:10.14503/thij-21-7572 · Cited by 22

Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records

Manzini, E., Vlacho, B., Franch‐Nadal, J., Escudero, J., Génova, A., Reixach, E., Andrés, E., Pizarro, I., Portero, J. L., Mauricio, D., et al. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104218 · Cited by 26

Comparative analysis of machine learning vs. traditional modeling approaches for predicting in-hospital mortality after cardiac surgery: temporal and spatial external validation based on a nationwide cardiac surgery registry

Zeng, J., Zhang, D., Lin, S., Su, X., Wang, P., Zhao, Y., & Zheng, Z. (2023) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcad028 · Cited by 22

Cardioinformatics: the nexus of bioinformatics and precision cardiology

Khomtchouk, B. B., Tran, D.-T. T., Vand, K. A., Might, M., Gozani, O., & Assimes, T. L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz119 · Cited by 23

History, current status, and future directions of artificial intelligence

Kubassova, O., Shaikh, F., Melus, C., & Mähler, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820239-5.00002-4 · Cited by 25

Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling

Lu, C., Wang, Y. G., Zaman, F. A., Wu, X., Adhaduk, M., Chang, A., Ji, J., Wei, T., Suksaranjit, P., Christodoulidis, G., et al. (2022) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-022-02564-5 · Cited by 22

Short-Term Therapies for Treatment of Acute and Advanced Heart Failure—Why so Few Drugs Available in Clinical Use, Why Even Fewer in the Pipeline?

Pollesello, P., Gal, T. B., Béttex, D., Černý, V., Colet, J. C., Eremenko, A. A., Farmakis, D. T., Fedele, F., Fonseca, C., Harjola, V., et al. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8111834 · Cited by 21

A Review on the Role of Artificial Intelligence in Stem Cell Therapy: An Initiative for Modern Medicines

Shende, P. K., & Devlekar, N. P. (2020) · Current Pharmaceutical Biotechnology · doi:10.2174/1389201021666201007122524 · Cited by 21

Envisioning the potential of Natural Language Processing (NLP) in Health Care Management

Sivarethinamohan, R., Sujatha, S., & Biswas, P. (2021) · International Engineering Conference on Sustainable Technology and Development (IEC) · doi:10.1109/iec52205.2021.9476131 · Cited by 22

The Emergence of Artificial Intelligence in Cardiology: Current and Future Applications

Kulkarni, P., Mahadevappa, M., & Chilakamarri, S. (2021) · Current Cardiology Reviews · doi:10.2174/1573403x17666211119102220 · Cited by 21

Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics

Raza, A., Chohan, T. A., Buabeid, M. A., Arafa, E. A., Chohan, T. A., Fatima, B., Sultana, K., Ullah, M. S., & Murtaza, G. (2022) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2022.2136244 · Cited by 21

A joint cross-dimensional contrastive learning framework for 12-lead ECGs and its heterogeneous deployment on SoC

Liu, W., Zhang, H., Chang, S., Wang, H., He, J. L., & Huang, Q. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106390 · Cited by 21

Machine Learning for Mortality Prediction in Patients With Heart Failure With Mildly Reduced Ejection Fraction

Tian, P., Liang, L., Zhao, X., Huang, B., Feng, J., Huang, L., Huang, Y., Zhai, M., Zhou, Q., Zhang, J., et al. (2023) · Journal of the American Heart Association · doi:10.1161/jaha.122.029124 · Cited by 24

Artificial Intelligence in Cardiology: An Australian Perspective

Jaltotage, B., Ihdayhid, A. R., Lan, N. S. R., Pathan, F. K., Patel, S. V., Arnott, C. G., Figtree, G. A., Kritharides, L., Islam, S. M. S., Chow, C. K., et al. (2023) · Heart Lung and Circulation · doi:10.1016/j.hlc.2023.06.703 · Cited by 21

Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI)

Yasin, P., Yimit, Y., Cai, X. T., Aimaiti, A., Sheng, W., Mamat, M., & Nijiati, M. (2024) · European journal of medical research · doi:10.1186/s40001-024-01988-0 · Cited by 22

Integrating predictive analytics in clinical trials: A paradigm shift in personalized medicine

Ojo, O. O., & Kiobel, B. (2024) · World Journal of Biology Pharmacy and Health Sciences · doi:10.30574/wjbphs.2024.19.3.0630 · Cited by 24

The Reform of Teaching Management Mode Based on Artificial Intelligence in the Era of Big Data

Shan-hui, D. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1533/4/042050 · Cited by 21

Application of Artificial Intelligence in Cardiovascular Medicine

Cheng, X., Manandhar, I., Aryal, S., & Joe, B. (2021) · Comprehensive physiology · doi:10.1002/cphy.c200034 · Cited by 21

Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets

Wegner, F. K., Vidal, M. L. B., Niehues, P., Willy, K., Radke, R., Garthe, P., Eckardt, L., Baumgartner, H., Diller, G., & Orwat, S. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11030690 · Cited by 20

Combining multi-dimensional molecular fingerprints to predict the hERG cardiotoxicity of compounds

Ding, W., Nan, Y., Wu, J., Han, C., Xin, X., Li, S., Liu, H., & Zhang, L. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105390 · Cited by 20

Inside the “black box”: Embedding clinical knowledge in data-driven machine learning for heart disease diagnosis

Meng, J., & Xing, R. (2022) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2022.10.005 · Cited by 20

A Novel Explainable AI Model for Medical Data Analysis

Shakhovska, N., SHEBEKO, A., & Prykarpatskyy, Y. (2024) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2024-0007 · Cited by 20

Ischemic Heart Disease Prognosis: A Hybrid Residual Attention-Enhanced LSTM Model

Cenitta, D., Arjunan, R. V., Paramasivam, G., Arul, N., Palkar, A., & Chadaga, K. (2024) · IEEE Access · doi:10.1109/access.2024.3524604 · Cited by 20

Better Patient Outcomes Through Mining of Biomedical Big Data

Suter‐Crazzolara, C. (2018) · Frontiers in ICT · doi:10.3389/fict.2018.00030 · Cited by 20

Artificial intelligence and cardiac surgery during COVID‐19 era

Khalsa, R. K., Khashkhusha, A., Zaidi, S., Harky, A., & Bashir, M. (2021) · Journal of Cardiac Surgery · doi:10.1111/jocs.15417 · Cited by 20

Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care

Variane, G. F. T., Camargo, J. P. V., Rodrigues, D. P., Magalhães, M., & Mimiça, M. J. (2022) · Frontiers in Pediatrics · doi:10.3389/fped.2021.755144 · Cited by 20

Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis

Shi, N., Liu, G., & Cao, M.-F. (2024) · International Journal of Ophthalmology · doi:10.18240/ijo.2024.03.02 · Cited by 19

Deep learning model for diagnosis of venous thrombosis from lower extremity peripheral ultrasound imaging

Chen, P., Tseng, B., Yang, Z.-H., Yu, C., Lin, K.-T., Chen, J., & Liu, P. (2024) · iScience · doi:10.1016/j.isci.2024.111318 · Cited by 19

Non-invasive technique for real-time myocardial infarction detection using faster R-CNN

Mohan, H., Rao, P. V., Kumara, H. C. S., & Manasa, S. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10957-2 · Cited by 19

Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair

Penso, M., Pepi, M., Mantegazza, V., Cefalù, C., Muratori, M., Fusini, L., Gripari, P., Alì, S. G., Caiani, E. G., & Tamborini, G. (2021) · Bioengineering · doi:10.3390/bioengineering8090117 · Cited by 20

Artificial Intelligence in Fractional-Order Systems Approximation with High Performances: Application in Modelling of an Isotopic Separation Process

Motorga, R.-M., Mureşan, V., Ungureșan, M.-L., Abrudean, M. I., Vălean, H., & Clitan, I. (2022) · Mathematics · doi:10.3390/math10091459 · Cited by 19

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches

Chopannejad, S., Sadoughi, F., Bagherzadeh, R., & Shekarchi, S. (2022) · Applied Clinical Informatics · doi:10.1055/a-1863-1589 · Cited by 19

Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis.

Li, X.-M., Gao, X., Tse, G., Hong, S., Chen, K., Li, G., & Liu, T. (2022) · PubMed · doi:10.11909/j.issn.1671-5411.2022.12.002 · Cited by 20

Artificial Intelligence Applications in Healthcare

Usmani, U. A., Happonen, A., Watada, J., & Khakurel, J. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-3091-3_89 · Cited by 23

Adopting Artificial Intelligence in Healthcare

Mouloudj, K., Vu Lan Oanh, L. E., Bouarar, A., Bouarar, A. C., Asanza, D. M., & Srivastava, M. (2023) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-6684-9324-3.ch001 · Cited by 19

Regulatory oversight, causal inference, and safe and effective health care machine learning

Stern, A. D., & Price, W. N. (2019) · Biostatistics · doi:10.1093/biostatistics/kxz044 · Cited by 20

A primer on artificial intelligence for the paediatric cardiologist

Gearhart, A. S., Gaffar, S., & Chang, A. C. (2020) · Cardiology in the Young · doi:10.1017/s1047951120001493 · Cited by 21

Reviewing the use and quality of machine learning in developing clinical prediction models for cardiovascular disease

Allan, S., Olaiya, R., & Burhan, R. (2021) · Postgraduate Medical Journal · doi:10.1136/postgradmedj-2020-139352 · Cited by 20

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

Maragna, R., Giacari, C. M., Guglielmo, M., Baggiano, A., Fusini, L., Guaricci, A. I., Rossi, A., Rabbat, M., & Pontone, G. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.736223 · Cited by 20

The Significance of Digitalisation and Artificial Intelligence in The Healthcare Sector: A Review

Mijwil, M. M., Faieq, A. K., & Al-Mistarehi, A.-H. W. (2022) · Asian Journal of Pharmacy Nursing and Medical Sciences · doi:10.24203/ajpnms.v10i3.7065 · Cited by 19

Artificial Intelligence in Cardiac Imaging

Seetharam, K., Shrestha, S., & Sengupta, P. P. (2020) · US Cardiology Review · doi:10.15420/usc.2019.19.2 · Cited by 20

Toward a Patient-Centered, Data-Driven Cardiology

Ribeiro, A. L., & Oliveira, G. M. M. D. (2019) · Arquivos Brasileiros de Cardiologia · doi:10.5935/abc.20190069 · Cited by 18

The Role of Artificial Intelligence in Exercise-Based Cardiovascular Health Interventions: A Scoping Review

Deligiannis, A. P., Sotiriou, P., Deligiannis, P., & Kouidi, E. J. (2025) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk10040409 · Cited by 18

Key Information Influencing Patient Decision-Making About AI in Health Care: Survey Experiment Study

Zhu, X., Stroud, A. M., Minteer, S. A., Yoo, D. W., Ridgeway, J. L., Mooghali, M., Miller, J. E., & Barry, B. A. (2025) · Journal of Medical Internet Research · doi:10.2196/75615 · Cited by 18

Machine learning, artificial intelligence and mechanical circulatory support: A primer for clinicians

Kanwar, M. K., Kilic, A., & Mehra, M. R. (2021) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2021.02.016 · Cited by 18

Artificial intelligence: The new wave of innovation in EUS

Sun, S., Liu, E., & Bhutani, M. (2021) · Endoscopic Ultrasound · doi:10.4103/2303-9027.313802 · Cited by 18

Proteomics profiling reveals a distinct high-risk molecular subtype of hypertrophic cardiomyopathy

Liang, L. W., Raita, Y., Hasegawa, K., Fifer, M. A., Maurer, M. S., Reilly, M. P., & Shimada, Y. J. (2022) · Heart · doi:10.1136/heartjnl-2021-320729 · Cited by 18

Bridging the divide between data scientists and clinicians

Bastian, G., Baker, G. H., & Limon, A. (2022) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2022.100066 · Cited by 21

Deep learning-based quantitative estimation of lymphedema-induced fibrosis using three-dimensional computed tomography images

Son, H., Lee, S., Kim, K., Koo, K.-I., & Hwang, C. H. (2022) · Scientific Reports · doi:10.1038/s41598-022-19204-6 · Cited by 18

Machine learning algorithms identify hypokalaemia risk in people with hypertension in the United States National Health and Nutrition Examination Survey 1999–2018

Lin, Z., Cheng, Y. T., & Cheung, B. M. Y. (2023) · Annals of Medicine · doi:10.1080/07853890.2023.2209336 · Cited by 18

Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease

Chinni, B. K., & Manlhiot, C. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.07.026 · Cited by 19

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