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

The practical implementation of artificial intelligence technologies in medicine

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246

Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects

Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020) · Business Process Management Journal · doi:10.1108/bpmj-10-2019-0411 · Cited by 1,280

Privacy and artificial intelligence: challenges for protecting health information in a new era

Murdoch, B. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00687-3 · Cited by 991

The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare

Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W. (2021) · British Medical Bulletin · doi:10.1093/bmb/ldab016 · Cited by 511

The Recent Progress and Applications of Digital Technologies in Healthcare: A Review

Senbekov, M., Saliev, T. M., Bukeyeva, Z., Almabayeva, A., Zhanaliyeva, M., Джамбуловна, А. Н., Toishibekov, Y., & Fakhradiyev, I. R. (2020) · International Journal of Telemedicine and Applications · doi:10.1155/2020/8830200 · Cited by 513

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Nia, N. G., Kaplanoğlu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 498

Deep learning for cardiovascular medicine: a practical primer

Krittanawong, C., Johnson, K. W., Rosenson, R. S., Wang, Z., Aydar, M., Baber, U., Min, J. K., Tang, W. H. W., Halperin, J. L., & Narayan, S. M. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz056 · Cited by 452

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Battineni, G., Sagaro, G. G., Chintalapudi, N., & Amenta, F. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10020021 · Cited by 418

Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review

Rasheed, K., Qayyum, A., Qadir, J., Sivathamboo, S., Kwan, P., Kuhlmann, L., O'Brien, T. J., & Razi, A. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3008792 · Cited by 438

State-of-the-Art Deep Learning in Cardiovascular Image Analysis

Litjens, G. J. S., Ciompi, F., Wolterink, J. M., de Vos, B. D., Leiner, T., Teuwen, J., & Išgum, I. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2019.06.009 · Cited by 399

A survey on deep learning in medicine: Why, how and when?

Piccialli, F., Somma, V. D., Giampaolo, F., Cuomo, S., & Fortino, G. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.09.006 · Cited by 369

Machine Learning and the Future of Cardiovascular Care

Quer, G., Arnaout, R., Henne, M., & Arnaout, R. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.11.030 · Cited by 353

Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization: The LINK-HF Multicenter Study

Stehlik, J., Schmalfuss, C., Bozkurt, B., Nativi-Nicolau, J., Wohlfahrt, P., Wegerich, S., Rose, K., Ray, R., Schofield, R., Deswal, A., et al. (2020) · Circulation Heart Failure · doi:10.1161/circheartfailure.119.006513 · Cited by 333

Digital transformation: A multidisciplinary perspective and future research agenda

Paul, J., Ueno, A., Dennis, C., Alamanos, E., Curtis, L. J., Foroudi, P., Kacprzak, A., Kunz, W. H., Liu, J., Marvi, R., et al. (2024) · International Journal of Consumer Studies · doi:10.1111/ijcs.13015 · Cited by 376

A study of artificial intelligence on employee performance and work engagement: the moderating role of change leadership

Wıjayatı, D. T., Rahman, Z., Fahrullah, A., Rahman, M. F. W., Arifah, I. D. C., & Kautsar, A. (2022) · International Journal of Manpower · doi:10.1108/ijm-07-2021-0423 · Cited by 347

Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review

Payrovnaziri, S. N., Chen, Z., Rengifo‐Moreno, P., Miller, T., Bian, J., Chen, J. H., Liu, X., & He, Z. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa053 · Cited by 335

Artificial Intelligence in Cardiology: Present and Future

López-Jiménez, F. A., Attia, Z. I., Arruda-Olson, A. M., Carter, R. E., Chareonthaitawee, P., Jouni, H., Kapa, S., Lerman, A., Luong, C. L., Medina‐Inojosa, J. R., et al. (2020) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2020.01.038 · Cited by 293

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101213 · Cited by 287

Machine learning in cardiovascular magnetic resonance: basic concepts and applications

Leiner, T., Rueckert, D., Suinesiaputra, A., Baeßler, B., Nezafat, R., Išgum, I., & Young, A. A. (2019) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-019-0575-y · Cited by 289

Leveraging Artificial Intelligence in Business: Implications, Applications and Methods

Sestino, A., & Mauro, A. D. (2021) · Technology Analysis & Strategic Management · doi:10.1080/09537325.2021.1883583 · Cited by 190

Artificial intelligence and echocardiography

Alsharqi, M., Woodward, W. J., Mumith, J. A., Markham, D. C., Upton, R., & Leeson, P. (2018) · Echo Research and Practice · doi:10.1530/erp-18-0056 · Cited by 270

Deep learning and the electrocardiogram: review of the current state-of-the-art

Somani, S. S., Russak, A. J., Richter, F., Zhao, S., Vaid, A., Chaudhry, F. F., Freitas, J. K. D., Naik, N., Miotto, R., Nadkarni, G. N., et al. (2020) · EP Europace · doi:10.1093/europace/euaa377 · Cited by 276

Cost-sensitive learning for imbalanced medical data: a review

Araf, I., Idri, A., & Chairi, I. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10652-8 · Cited by 276

Artificial intelligence in reproductive medicine

Wang, R., Pan, W. D., Jin, L., Li, Y., Geng, Y., Gao, C., Chen, G., Wang, H., Ma, D., & Liao, S. (2019) · Reproduction · doi:10.1530/rep-18-0523 · Cited by 263

AI-driven healthcare: A review on ensuring fairness and mitigating bias

Chinta, S. V., Wang, Z., Palikhe, A., Zhang, X., Kashif, A., Smith, M. A., Liu, J., & Zhang, W. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000864 · Cited by 268

Artificial Intelligence in Healthcare: Review, Ethics, Trust Challenges & Future Research Directions

Kumar, P., Chauhan, S., & Awasthi, L. K. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105894 · Cited by 259

Design And Implementing Heart Disease Prediction Using Naives Bayesian

Repaka, A. N., Ravikanti, S. D., & Franklin, R. G. (2019) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei.2019.8862604 · Cited by 234

Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist

Sengupta, P. P., Shrestha, S., Berthon, B., Messas, E., Donal, E., Tison, G. H., Min, J. K., D’hooge, J., Voigt, J., Dudley, J. T., et al. (2020) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2020.07.015 · Cited by 230

Artificial Intelligence, Machine Learning, and Cardiovascular Disease

Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020) · Clinical Medicine Insights Cardiology · doi:10.1177/1179546820927404 · Cited by 228

Explainability, transparency and black box challenges of AI in radiology: impact on patient care in cardiovascular radiology

Marey, A., Arjmand, P., alerab, A. D. S., Eslami, M. J., Saad, A. M., Sanchez, N., & Umair, M. (2024) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-024-01356-2 · Cited by 243

Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia

Mirzaei, G., & Adeli, H. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103293 · Cited by 219

Artificial intelligence in the field of pharmacy practice: A literature review

Chalasani, S. H., Syed, J. M., Ramesh, M., Patil, V., & Kumar, T. M. P. (2023) · Exploratory Research in Clinical and Social Pharmacy · doi:10.1016/j.rcsop.2023.100346 · Cited by 231

Benefits and Risks of AI in Health Care: Narrative Review

Chustecki, M. (2024) · Interactive Journal of Medical Research · doi:10.2196/53616 · Cited by 208

Artificial Intelligence in the Intensive Care Unit

Gutiérrez, G. (2020) · Critical Care · doi:10.1186/s13054-020-2785-y · Cited by 206

Artificial intelligence in abdominal aortic aneurysm

Raffort, J., Adam, C., Carrier, M., Ballaith, A., Coscas, R., Jean‐Baptiste, E., Hassen‐Khodja, R., Chakfé, N., & Lareyre, F. (2020) · Journal of Vascular Surgery · doi:10.1016/j.jvs.2019.12.026 · Cited by 202

Deep Learning in Cardiology

Bizopoulos, P., & Koutsouris, D. (2018) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2018.2885714 · Cited by 199

Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients

Diller, G., Kempny, A., Babu‐Narayan, S. V., Henrichs, M., Brida, M., Uebing, A. S., Lammers, A. E., Baumgartner, H., Li, W., Wort, S. J., et al. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy915 · Cited by 191

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Singh, M., Kumar, A., Khanna, N. N., Laird, J. R., Nicolaides, A. N., Faa, G., Johri, A. M., Mantella, L. E., Fernandes, J. F. E., Teji, J. S., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102660 · Cited by 197

Current state and future prospects of artificial intelligence in ophthalmology: a review

Hogarty, D. T., Mackey, D. A., & Hewitt, A. W. (2018) · Clinical and Experimental Ophthalmology · doi:10.1111/ceo.13381 · Cited by 181

Cardiovascular calcification: artificial intelligence and big data accelerate mechanistic discovery

Rogers, M. A., & Aïkawa, E. (2018) · Nature Reviews Cardiology · doi:10.1038/s41569-018-0123-8 · Cited by 173

Health Information Management: Implications of Artificial Intelligence on Healthcare Data and Information Management

Stanfill, M. H., & Marc, D. T. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677913 · Cited by 191

Artificial Intelligence Revolutionizing the Field of Medical Education

Narayanan, S., Ramakrishnan, R., Durairaj, E., & Das, A. (2023) · Cureus · doi:10.7759/cureus.49604 · Cited by 164

Progression of Early Subclinical Atherosclerosis (PESA) Study

Ibáñez, B., Fernández‐Ortíz, A., Fernández‐Friera, L., García‐Lunar, I., Andrés, V., & Fuster, V. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.05.011 · Cited by 171

Artificial Intelligence for the Diagnosis of Heart Failure

Choi, D.-J., Park, J. J., Ali, T., & Lee, S. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0261-3 · Cited by 163

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

Olsen, C. R., Mentz, R. J., Anstrom, K. J., Page, D. I., & Patel, P. A. (2020) · American Heart Journal · doi:10.1016/j.ahj.2020.07.009 · Cited by 164

Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal

Rahimi, S. A., Légaré, F., Sharma, G., Archambault, P. M., Zomahoun, H. T. V., Chandavong, S., Rhéault, N., Wong, S. T., Langlois, L., Couturier, Y., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/29839 · Cited by 169

Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis

Zhou, J., Du, M., Chang, S., & Chen, Z. (2021) · Cardiovascular Ultrasound · doi:10.1186/s12947-021-00261-2 · Cited by 155

Smart Technologies used as Smart Tools in the Management of Cardiovascular Disease and their Future Perspective

Ullah, M., Hamayun, S., Wahab, A., Khan, S. U., Rehman, M. U., Haq, Z. U., Rehman, K. U., Ullah, A., Mehreen, A., Awan, U. A., et al. (2023) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2023.101922 · Cited by 150

Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study

Commandeur, F., Slomka, P. J., Goeller, M., Chen, X., Cadet, S. J., Razipour, A., McElhinney, P. A., Gransar, H., Cantú, S. M., Miller, R. J. H., et al. (2019) · Cardiovascular Research · doi:10.1093/cvr/cvz321 · Cited by 153

Multi-task deep learning for cardiac rhythm detection in wearable devices

Soto, J. T., & Ashley, E. A. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00320-4 · Cited by 148

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Hassan, C. A. U., Iqbal, J., Irfan, R., Hussain, S., Algarni, A. D., Bukhari, S. S. H., Alturki, N. M., & Ullah, S. S. (2022) · Sensors · doi:10.3390/s22197227 · Cited by 148

An Explainable Transformer-Based Deep Learning Model for the Prediction of Incident Heart Failure

Rao, S., Li, Y., Ramakrishnan, R., Hassaïne, A., Canoy, D., Cleland, J. G. F., Lukasiewicz, T., Salimi‐Khorshidi, G., & Rahimi, K. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3148820 · Cited by 145

Artificial intelligence in pancreatic cancer

Huang, B., Huang, H., Zhang, S., Zhang, D., Shi, Q., Liu, J., & Guo, J. (2022) · Theranostics · doi:10.7150/thno.77949 · Cited by 142

Artificial Intelligence in Nutrients Science Research: A Review

Sak, J. J., & Suchodolska, M. (2021) · Nutrients · doi:10.3390/nu13020322 · Cited by 144

Impact of Artificial Intelligence on Interventional Cardiology

Sardar, P., Abbott, J. D., Kundu, A., Aronow, H. D., Granada, J. F., & Giri, J. (2019) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2019.04.048 · Cited by 151

Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation

Tiwari, P., Colborn, K. L., Smith, D. E., Xing, F., Ghosh, D., & Rosenberg, M. A. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.19396 · Cited by 138

Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification

Kwon, J., Kim, K., Jeon, K., Kim, H. M., Kim, M. J., Lim, S., Song, P. S., Park, J., Choi, R. K., & Oh, B. (2019) · Korean Circulation Journal · doi:10.4070/kcj.2018.0446 · Cited by 135

Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease

Otaki, Y., Singh, A., Kavanagh, P., Miller, R. J. H., Parekh, T., Tamarappoo, B. K., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., et al. (2021) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2021.04.030 · Cited by 132

Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention

Hoogeveen, R. M., Pereira, J. P. B., Nurmohamed, N. S., Zampoleri, V., Bom, M. J., Baragetti, A., Boekholdt, S. M., Knaapen, P., Khaw, K., Wareham, N. J., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa648 · Cited by 133

Precision Medicine and the future of Cardiovascular Diseases: A Clinically Oriented Comprehensive Review

Sethi, Y., Patel, N., Kaka, N., Kaiwan, O., Kar, J., Moinuddin, A., Goel, A., Chopra, H., & Cavalu, S. D. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12051799 · Cited by 135

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

Regueira‐Iglesias, A., Balsa‐Castro, C., Blanco‐Pintos, T., & Tomás, I. (2023) · Molecular Oral Microbiology · doi:10.1111/omi.12434 · Cited by 146

Linear discriminant analysis and principal component analysis to predict coronary artery disease

Ricciardi, C., Valente, A. S., Edmund, K., Cantoni, V., Green, R., Fiorillo, A., Picone, I., Santini, S., & Cesarelli, M. (2020) · Health Informatics Journal · doi:10.1177/1460458219899210 · Cited by 127

Machine Learning in Arrhythmia and Electrophysiology

Trayanova, N. A., Popescu, D. M., & Shade, J. K. (2021) · Circulation Research · doi:10.1161/circresaha.120.317872 · Cited by 128

Application of Artificial Intelligence in Shared Decision Making: Scoping Review

Rahimi, S. A., Cwintal, M., Huang, Y., Ghadiri, P., Grad, R. M., Poenaru, D., Gore, G. C., Zomahoun, H. T. V., Légaré, F., & Pluye, P. (2022) · JMIR Medical Informatics · doi:10.2196/36199 · Cited by 136

GPT-4 artificial intelligence model outperforms ChatGPT, medical students, and neurosurgery residents on neurosurgery written board-like questions

Guerra, G. A., Hofmann, H., Sobhani, S., Hofmann, G., Gomez, D., Soroudi, D., Hopkins, B. S., Dallas, J., Pangal, D. J., Cheok, S., et al. (2023) · World Neurosurgery · doi:10.1016/j.wneu.2023.08.042 · Cited by 123

Artificial intelligence in radiotherapy

Siddique, S., & Chow, J. C. L. (2020) · Reports of Practical Oncology & Radiotherapy · doi:10.1016/j.rpor.2020.03.015 · Cited by 126

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23020828 · Cited by 120

Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram

Vaid, A., Johnson, K. W., Badgeley, M. A., Somani, S. S., Bicak, M., Landi, I., Russak, A. J., Zhao, S., Levin, M. A., Freeman, R., et al. (2021) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2021.08.004 · Cited by 124

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Πετμεζάς, Γ., Stefanopoulos, L., Kilintzis, V., Tzavelis, A., Rogers, J. A., Katsaggelos, A. K., & Maglaveras, N. (2022) · JMIR Medical Informatics · doi:10.2196/38454 · Cited by 126

Artificial Intelligence Applications for Friction Stir Welding: A Review

Eren, B., Güvenç, M. A., & Mıstıkoğlu, S. (2020) · Metals and Materials International · doi:10.1007/s12540-020-00854-y · Cited by 117

Update on heart failure management and future directions

Choi, H., Park, M., & Youn, J. (2018) · The Korean Journal of Internal Medicine · doi:10.3904/kjim.2018.428 · Cited by 117

Lifespan Perspective on Congenital Heart Disease Research

Diller, G., Arvanitaki, A., Opotowsky, A. R., Jenkins, K. J., Moons, P., Kempny, A., Tandon, A. A., Redington, A. N., Khairy, P., Mital, S. R., et al. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.03.012 · Cited by 114

Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database

Nudel, J., Bishara, A. M., de Geus, S. W. L., Patil, P., Srinivasan, J., Hess, D. T., & Woodson, J. (2020) · Surgical Endoscopy · doi:10.1007/s00464-020-07378-x · Cited by 113

Application of mobile health, telemedicine and artificial intelligence to echocardiography

Seetharam, K., Kagiyama, N., & Sengupta, P. P. (2019) · Echo Research & Practice · doi:10.1530/erp-18-0081 · Cited by 111

Estimation of realistic renewable and non-renewable energy use targets for livestock production systems utilising an artificial neural network method: A step towards livestock sustainability

Elahi, E., Cui, W., Jha, S. K., & Zhang, H. (2019) · Energy · doi:10.1016/j.energy.2019.06.084 · Cited by 111

Pneumonia Detection Using Deep Learning Based on Convolutional Neural Network

Racic, L., Popović, T., Čakić, S., & Šandi, S. (2021) · doi:10.1109/it51528.2021.9390137 · Cited by 111

Applications of Artificial Intelligence in Cardiology. The Future is Already Here

Dorado-Díaz, P. I., Sampedro-Gómez, J., Vicente-Palacios, V., & Sánchez, P. L. (2019) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2019.05.014 · Cited by 111

Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: proceedings of the European Society of Cardiology cardiovascular round table

Leclercq, C., Witt, H., Hindricks, G., Katra, R. P., Albert, D., Belliger, A., Cowie, M. R., Deneke, T., Friedman, P., Haschemi, M., et al. (2022) · EP Europace · doi:10.1093/europace/euac052 · Cited by 109

Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography

Kwon, J.-M., Kim, K.-H., Medina-Inojosa, J., Jeon, K.-H., Park, J., & Oh, B.-H. (2020) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2020.04.009 · Cited by 108

Applications of artificial intelligence and machine learning in heart failure

Averbuch, T., Sullivan, K., Sauer, A. J., Mamas, M. A., Voors, A. A., Gale, C. P., Metra, M., Ravindra, N. G., & Spall, H. G. C. V. (2022) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztac025 · Cited by 106

Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management

Wu, Z., & Guo, C. (2025) · BioMedical Engineering OnLine · doi:10.1186/s12938-025-01349-w · Cited by 116

A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome

Sherazi, S. W. A., Bae, J., & Lee, J. Y. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249338 · Cited by 106

A Review of Biomedical Devices: Classification, Regulatory Guidelines, Human Factors, Software as a Medical Device, and Cybersecurity

Tettey, F., Parupelli, S. K., & Desai, S. S. (2023) · Biomedical Materials & Devices · doi:10.1007/s44174-023-00113-9 · Cited by 102

Artificial Intelligence and Heart Failure: A State-of-the-Art Review

Khan, M. S., Arshad, M. S., Greene, S. J., Spall, H. G. C. V., Pandey, A., Vemulapalli, S., Perakslis, E. D., & Butler, J. (2023) · European Journal of Heart Failure · doi:10.1002/ejhf.2994 · Cited by 109

The primary use of artificial intelligence in cardiovascular diseases: what kind of potential role does artificial intelligence play in future medicine?

Yang, Y., Zhang, J., Zang, G., & Pu, J. (2019) · PubMed Central · doi:10.11909/j.issn.1671-5411.2019.08.010 · Cited by 107

Glaucoma management in the era of artificial intelligence

Devalla, S. K., Zhang, L., Pham, T. H., Boote, C., Strouthidis, N. G., Thiéry, A. H., & Girard, M. J. A. (2019) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2019-315016 · Cited by 98

Artificial intelligence in cardiology

Itchhaporia, D. (2020) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2020.11.007 · Cited by 98

Artificial Intelligence in Genetics

Vilhekar, R. S., & Rawekar, A. (2024) · Cureus · doi:10.7759/cureus.52035 · Cited by 104

Next-generation business models for artificial intelligence start-ups in the healthcare industry

Kulkov, I. (2023) · International Journal of Entrepreneurial Behavior & Research · doi:10.1108/ijebr-04-2021-0304 · Cited by 97

Deep‐learning artificial intelligence analysis of clinical variables predicts mortality in COVID‐19 patients

Zhu, J., Ge, P., Jiang, C., Zhang, Y., Li, X., Zhao, Z., Zhang, L., & Duong, T. Q. (2020) · Journal of the American College of Emergency Physicians Open · doi:10.1002/emp2.12205 · Cited by 96

The impact of artificial intelligence on remote healthcare: Enhancing patient engagement, connectivity, and overcoming challenges

Chaturvedi, U., Chauhan, S. B., & Singh, I. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.12.003 · Cited by 104

Current applications of big data and machine learning in cardiology.

Cuocolo, R., Perillo, T., Rosa, E. D., Ugga, L., & Petretta, M. (2019) · PubMed Central · doi:10.11909/j.issn.1671-5411.2019.08.002 · Cited by 97

Artificial Intelligence and Hypertension: Recent Advances and Future Outlook

Chaikijurajai, T., Laffin, L. J., & Tang, W. H. W. (2020) · American Journal of Hypertension · doi:10.1093/ajh/hpaa102 · Cited by 94

Towards revolutionizing precision healthcare: A systematic literature review of artificial intelligence methods in precision medicine

Abbaoui, W., Retal, S., Bhiri, B. E., Kharmoum, N., & Ziti, S. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101475 · Cited by 95

Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems

Mhamdi, L., Dammak, O., Cottin, F., & Dhaou, I. B. (2022) · Biomedicines · doi:10.3390/biomedicines10082013 · Cited by 91

Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications

Gautam, N., Ghanta, S. N., Mueller, J. M., Mansour, M. M., Chen, Z., Puente, C., Ha, Y., Tarun, T., Dhar, G., Sivakumar, K., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12122964 · Cited by 88

Revolutionizing Healthcare: The Role of Machine Learning in the Health Sector

Sarker, M. (2024) · Journal of Artificial Intelligence General science (JAIGS) · doi:10.60087/jaigs.v2i1.96 · Cited by 91

Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review

Jung, J., Lee, H., Jung, H., & Kim, H. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16110 · Cited by 87

Real-Time Artificial Intelligence–Based Guidance of Echocardiographic Imaging by Novices: Image Quality and Suitability for Diagnostic Interpretation and Quantitative Analysis

Mor‐Avi, V., Khandheria, B. K., Klempfner, R., Cotella, J. I., Moreno, M., Ignatowski, D., Guile, B., Hayes, H. J., Hipke, K., Kaminski, A., et al. (2023) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.123.015569 · Cited by 87

Advances in machine learning-assisted SERS sensing towards food safety and biomedical analysis

Dong, Y., Hu, J., Jin, J., Zhou, H., Jin, S., & Yang, D. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.117974 · Cited by 87

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