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 most efficient machine learning algorithms in stroke prediction: A systematic review

Asadi, F., Rahimi, M., Daeechini, A. H., & Paghe, A. (2024) · Health Science Reports · doi:10.1002/hsr2.70062 · Cited by 42

Artificial intelligence in cardiovascular medicine: An updated review of the literature

Zargarzadeh, A., Javanshir, E., Ghaffari, A., Mosharkesh, E., & Anari, B. (2023) · Journal of Cardiovascular and Thoracic Research · doi:10.34172/jcvtr.2023.33031 · Cited by 41

Pragmatic considerations for fostering reproducible research in artificial intelligence

Carter, R. E., Attia, Z. I., López-Jiménez, F. A., & Friedman, P. A. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0120-2 · Cited by 48

Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation

Nava, A., Atienza, F., Bermejo, J., & Fernández‐Avilés, F. (2021) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00764.2020 · Cited by 43

Utilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure

Johnson, A. E., Brewer, L. C., Echols, M. R., Mazimba, S., Shah, R. U., & Breathett, K. K. (2022) · Heart Failure Clinics · doi:10.1016/j.hfc.2021.11.001 · Cited by 41

Soft Transducer for Patient’s Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection

Arpaïa, P., Crauso, F., Benedetto, E. D., Duraccio, L., Improta, G., & Serino, F. (2022) · Sensors · doi:10.3390/s22020536 · Cited by 42

From evidence-based medicine to digital twin technology for predicting ventricular tachycardia in ischaemic cardiomyopathy

de Lepper, A. G. W., Buck, C. M. A., Veer, M. V. ’., Huberts, W., van de Vosse, F. N., & Dekker, L. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0317 · Cited by 40

The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction

Asaad, M., Lu, S.-C., Hassan, A. M., Kambhampati, P. V., Mitchell, D., Chang, E. I.-F., Yu, P., Hanasono, M. M., & Sidey-Gibbons, C. J. (2023) · Annals of Surgical Oncology · doi:10.1245/s10434-022-13053-3 · Cited by 42

Machine Learning for Diagnosis of Pulmonary Hypertension by Echocardiography

Anand, V., Weston, A. D., Scott, C. G., Kane, G. C., Pellikka, P. A., & Carter, R. E. (2024) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2023.05.006 · Cited by 40

Applications of machine learning in decision analysis for dose management for dofetilide

Levy, A. E., Biswas, M., Weber, R. A., Tarakji, K. G., Chung, M. K., Noseworthy, P. A., Newton‐Cheh, C., & Rosenberg, M. A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0227324 · Cited by 41

Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data

Merayo, D., Rodríguez-Prieto, Á., & Camacho, A. M. (2020) · Metals · doi:10.3390/met10070904 · Cited by 40

Current and Future Use of Membrane Technology in the Traditional Chinese Medicine Industry

Liu, H., Li, B., Guo, L., Pan, L., Zhu, H.-X., Tang, Z., Xing, W., Cai, Y.-Y., Duan, J., Wang, M., et al. (2021) · Separation and Purification Reviews · doi:10.1080/15422119.2021.1995875 · Cited by 40

Artificial intelligence in cardiology: The past, present and future

Gupta, M. D., Kunal, S., Girish, M. P., Gupta, A., & Yadav, R. K. (2022) · Indian Heart Journal · doi:10.1016/j.ihj.2022.07.004 · Cited by 41

Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age

Burgos-Artizzu, X. P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B., Vellvé, K., Eixarch, E., Crispi, F., Bonet-Carne, E., Bennasar, M., & Gratacos, E. (2021) · American Journal of Obstetrics & Gynecology MFM · doi:10.1016/j.ajogmf.2021.100462 · Cited by 39

Machine learning in cardiology: Clinical application and basic research

Komuro, J., Kusumoto, D., Hashimoto, H., & Yuasa, S. (2023) · Journal of Cardiology · doi:10.1016/j.jjcc.2023.04.020 · Cited by 41

Artificial intelligence in cardiovascular prevention: new ways will open new doors

Ciccarelli, M., Giallauria, F., Carrizzo, A., Visco, V., Silverio, A., Cesaro, A., Calabrò, P., LUCA, N. D., Mancusi, C., Masarone, D., et al. (2023) · Journal of Cardiovascular Medicine · doi:10.2459/jcm.0000000000001431 · Cited by 39

Amalgamation of Artificial Intelligence with Nanoscience for Biomedical Applications

Kasture, K., & Shende, P. K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09948-3 · Cited by 39

Heart Failure Management through Telehealth: Expanding Care and Connecting Hearts

Tedeschi, A., Palazzini, M., Trimarchi, G., Conti, N., Spigno, F. D., Gentile, P., D’Angelo, L., Garascia, A., Ammirati, E., Morici, N., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13092592 · Cited by 41

Diabetic retinopathy detection using supervised and unsupervised deep learning: a review study

Naz, H. U., Ahuja, N. J., & Nijhawan, R. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10770-x · Cited by 39

A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder

Kou, W., Carlson, D. A., Baumann, A. J., Donnan, E. N., Luo, Y., Pandolfino, J. E., & Etemadi, M. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.102006 · Cited by 39

Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data

Simon, S. T., Mandair, D., Tiwari, P., & Rosenberg, M. A. (2021) · Journal of Cardiovascular Pharmacology and Therapeutics · doi:10.1177/1074248421995348 · Cited by 41

A multi-stage machine learning model for diagnosis of esophageal manometry

Kou, W., Carlson, D. A., Baumann, A. J., Donnan, E. N., Schauer, J. M., Etemadi, M., & Pandolfino, J. E. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102233 · Cited by 39

Artificial intelligence in arthroplasty

Purnomo, G., Yeo, S.-J., & Liow, M. H. L. (2021) · Arthroplasty · doi:10.1186/s42836-021-00095-3 · Cited by 39

Machine learning for intelligent welding and manufacturing systems: research progress and perspective review

Kumar, S., Gaur, V., & Wu, C. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-10403-z · Cited by 38

Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography

Maturi, B., Dulal, S., Ibrahim, A., Ramakrishna, M., Chinta, V., Nanda, N. C., Sharma, A. K., & Ravipati, H. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020625 · Cited by 39

Digital cardiovascular care in COVID‐19 pandemic: A potential alternative?

Kaushik, A., Patel, S., & Dubey, K. (2020) · Journal of Cardiac Surgery · doi:10.1111/jocs.15094 · Cited by 38

Artificial Intelligence and Transcatheter Interventions for Structural Heart Disease: A glance at the (near) future

Ribeiro, J. M., Astudillo, P., Backer, O. D., Budde, R. P. J., Nuis, R. J., Goudzwaard, J., Mieghem, N. M. V., Lumens, J., Mortier, P., Mattace-Raso, F. U. S., et al. (2021) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2021.02.002 · Cited by 39

Regulatory aspects of artificial intelligence and machine learning-enabled software as medical devices (SaMD)

Mähler, M., Auza, C., Albesa, R., Melus, C., & Wu, J. A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820239-5.00010-3 · Cited by 38

Artificial Intelligence in Cardiovascular Imaging: “Unexplainable” Legal and Ethical Challenges?

Lang, M., Bernier, A., & Knoppers, B. M. (2022) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.10.009 · Cited by 37

Machine learning integration of multimodal data identifies key features of blood pressure regulation

Louca, P., Tran, T. Q. B., Toit, C. D., Christofidou, P., Spector, T. D., Mangino, M., Suhre, K., Padmanabhan, S., & Menni, C. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104243 · Cited by 37

Robotics, imaging, and artificial intelligence in the catheterisation laboratory

Beyar, R., Davies, J. E., Cook, C., Dudek, D., Cummins, P. A., & Bruining, N. (2021) · EuroIntervention · doi:10.4244/eij-d-21-00145 · Cited by 38

Intelligent support in manufacturing process selection based on artificial neural networks, fuzzy logic, and genetic algorithms: Current state and future perspectives

Mumali, F., & Kałkowska, J. (2024) · Computers & industrial engineering · doi:10.1016/j.cie.2024.110272 · Cited by 29

Artificial Intelligence in the Intensive Care Unit

Gutiérrez, G. (2020) · Annual update in intensive care and emergency medicine · doi:10.1007/978-3-030-37323-8_51 · Cited by 37

A Machine Learning Approach Using Statistical Models for Early Detection of Cardiac Arrest in Newborn Babies in the Cardiac Intensive Care Unit

Gupta, K. P., Jiwani, N., Pau, G., & Alibakhshikenari, M. (2023) · IEEE Access · doi:10.1109/access.2023.3286346 · Cited by 37

How Artificial Intelligence Could Redefine Clinical Trials in Cardiovascular Medicine: Lessons Learned from Oncology

Krittanawong, C., Johnson, K. W., & Tang, W. H. W. (2019) · Personalized Medicine · doi:10.2217/pme-2018-0130 · Cited by 37

Applications of Artificial Intelligence in Healthcare

Quazi, S., Saha, R. P., & Singh, M. K. (2022) · Journal of Experimental Biology and Agricultural Sciences · doi:10.18006/2022.10(1).211.226 · Cited by 36

Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias

Akter, S., Liu, Z., Simões, E. J., & Rao, P. R. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2025.100169 · Cited by 37

Aplicaciones de la inteligencia artificial en cardiología: el futuro ya está aquí

Dorado-Díaz, P. I., Sampedro-Gómez, J., Vicente-Palacios, V., & Sanchez, P. L. (2019) · Revista Española de Cardiología · doi:10.1016/j.recesp.2019.05.016 · Cited by 40

Cardiac risk stratification in cancer patients: A longitudinal patient–patient network analysis

Hou, Y., Zhou, Y., Hussain, M., Budd, G. T., Tang, W. H. W., Abraham, J., Xu, B., Shah, C., Moudgil, R., Popović, Z., et al. (2021) · PLoS Medicine · doi:10.1371/journal.pmed.1003736 · Cited by 38

ChatGPT: A Conceptual Review of Applications and Utility in the Field of Medicine

Rao, S. J., Isath, A., Krishnan, P., Tangsrivimol, J. A., Virk, H. U. H., Wang, Z., Glicksberg, B. S., & Krittanawong, C. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02075-x · Cited by 35

Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning

Bock, C., Walter, J. E., Rieck, B., Strebel, I., Rumora, K., Schaefer, I., Zellweger, M. J., Borgwardt, K. M., & Mueller, C. E. (2024) · Nature Communications · doi:10.1038/s41467-024-49390-y · Cited by 35

Cardiac magnetic resonance imaging: the future is bright

Seetharam, K., & Lerakis, S. (2019) · F1000Research · doi:10.12688/f1000research.19721.1 · Cited by 35

Artificial Intelligence and Machine Learning in Cardiovascular Imaging

Seetharam, K., & Min, J. K. (2020) · Methodist DeBakey Cardiovascular Journal · doi:10.14797/mdcj-16-4-263 · Cited by 34

Health Technology Assessment for Cardiovascular Digital Health Technologies and Artificial Intelligence: Why Is It Different?

Vervoort, D., Tam, D. Y., & Wijeysundera, H. C. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.08.015 · Cited by 37

Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance

Xue, H., Artico, J., Davies, R. H., Adam, R. D., Shetye, A. M., Augusto, J. B., Bhuva, A., Fröjdh, F., Wong, T. C., Fukui, M., et al. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.121.023849 · Cited by 37

Cardiovascular Events Prediction using Artificial Intelligence Models and Heart Rate Variability

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.07.030 · Cited by 34

Predictive modeling in spine surgery

Malik, A. T., & Khan, S. N. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2019.07.99 · Cited by 34

Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study

Rahman, T., Al-Ruweidi, M. K. A. A., Sumon, M. S. I., Kamal, R. Y., Chowdhury, M. E. H., & Yalcin, H. C. (2023) · IEEE Access · doi:10.1109/access.2023.3316719 · Cited by 37

The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support.

Ye, J., Woods, D. M., Jordan, N., & Starren, J. B. (2024) · PubMed Central · Cited by 34

Machine learning approaches for cardiovascular disease prediction: A review

Wan, S., Wan, F., & Dai, X. (2025) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2025.04.055 · Cited by 33

Denoising and artefact removal for transthoracic echocardiographic imaging in congenital heart disease: utility of diagnosis specific deep learning algorithms

Diller, G., Lammers, A. E., Babu‐Narayan, S. V., Li, W., Radke, R., Baumgartner, H., Gatzoulis, M. Α., & Orwat, S. (2019) · International journal of cardiac imaging · doi:10.1007/s10554-019-01671-0 · Cited by 33

Application of data mining technology in alarm analysis of communication network

Qun, Z., Li, Y., & Cao, J. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.08.012 · Cited by 33

Usefulness of machine learning in COVID-19 for the detection and prognosis of cardiovascular complications

Zimmerman, A., & Kalra, D. K. (2020) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm.2020.03.120 · Cited by 33

How to standardize the measurement of left ventricular ejection fraction

Kusunose, K., Zheng, R., Yamada, H., & Sata, M. (2021) · Journal of Medical Ultrasonics · doi:10.1007/s10396-021-01116-z · Cited by 37

Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence

Seetharam, K., Shrestha, S., & Sengupta, P. P. (2021) · Interventional Cardiology Reviews Research Resources · doi:10.15420/icr.2020.04 · Cited by 33

Development and Validation of a Machine Learning Predictive Model for Cardiac Surgery-Associated Acute Kidney Injury

Li, Q., Lv, H., Chen, Y., Shen, J., Shi, J., & Zhou, C. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12031166 · Cited by 37

Artificial Intelligence

Sheikh, A. Y., & Fann, J. I. (2019) · Thoracic surgery clinics/Thorac. surg. clin. · doi:10.1016/j.thorsurg.2019.03.011 · Cited by 33

The role of artificial intelligence in the application of the integrated electronic health records and patient-generated health dataPreprint

Ye, J., Hai, J., Song, J., & Wang, Z. (2024) · medRxiv · doi:10.1101/2024.05.01.24306690 · Cited by 36

Hydrogen‐Associated Multiple Electronic Phase Transitions for d‐Orbital Transitional Metal Oxides: Progress, Application, and Beyond

Zhou, X., Li, H., Jiao, Y., Zhou, G., Ji, H., Jiang, Y., & Xu, X. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202316536 · Cited by 32

A Mobile Crowd Sensing Application for Hypertensive Patients

Jovanović, S., Jovanović, M., Škorić, T., Jokić, S., Milovanović, B., Katzis, K., & Bajić, D. (2019) · Sensors · doi:10.3390/s19020400 · Cited by 32

Uses and opportunities for machine learning in hypertension research

Amaratunga, D., Cabrera, J., Sargsyan, D., Kostis, J. B., Zinonos, S., & Kostis, W. J. (2020) · International Journal of Cardiology Hypertension · doi:10.1016/j.ijchy.2020.100027 · Cited by 34

Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment

Navarini, L., Caso, F., Costa, L., Currado, D., Stola, L., Perrotta, F., Delfino, L., Sperti, M., Deriu, M. A., Ruscitti, P., et al. (2020) · Rheumatology and Therapy · doi:10.1007/s40744-020-00233-4 · Cited by 32

New Visual Expression of Anime Film Based on Artificial Intelligence and Machine Learning Technology

Wan, Y., & Ren, M. (2021) · Journal of Sensors · doi:10.1155/2021/9945187 · Cited by 32

Deep Explainable Hate Speech Active Learning on Social-Media Data

Ahmed, U., & Lin, J. C. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3165136 · Cited by 32

Artificial intelligence in atherosclerotic disease: Applications and trends

Kampaktsis, P. Ν., Emfietzoglou, M., Shehhi, A. A., Fasoula, N., Bakogiannis, C., Mouselimis, D., Tsarouchas, A., Vassilikos, V. P., Kallmayer, M. A., Eckstein, H., et al. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.949454 · Cited by 32

Applications of AI in Cardiovascular Disease Detection — A Review of the Specific Ways in which AI Is Being Used to Detect and Diagnose Cardiovascular Diseases

Srinivasan, S., & Sharma, V. (2025) · Wiley eBooks · doi:10.1002/9781394278695.ch6 · Cited by 35

The Impact of Medical Explainable Artificial Intelligence on Nurses’ Innovation Behaviour: A Structural Equation Modelling Approach

Li, X., Zong, Q., & Cheng, M. (2024) · Journal of Nursing Management · doi:10.1155/2024/8885760 · Cited by 33

Applications, challenges and future directions of artificial intelligence in cardio‐oncology

Ravera, F., Gilardi, N., Ballestrero, A., & Zoppoli, G. M. (2025) · European Journal of Clinical Investigation · doi:10.1111/eci.14370 · Cited by 32

Big data, artificial intelligence, and cardiovascular precision medicine

Krittanawong, C., Johnson, K. W., Hershman, S. G., & Tang, W. H. W. (2018) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2018.1528871 · Cited by 31

Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications

Langlais, É. L., Thériault-Lauzier, P., Marquis‐Gravel, G., Kulbay, M., So, D. Y. F., Tanguay, J., Ly, H. Q., Gallo, R., Lesage, F., & Avram, R. (2022) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-022-10260-x · Cited by 35

Evaluation of an Ambulatory ECG Analysis Platform Using Deep Neural Networks in Routine Clinical Practice

Fiorina, L., Maupain, C., Gardella, C., Manenti, V., Salerno, F., Socié, P., Li, J., Henry, C., Plesse, A., Narayanan, K., et al. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.122.026196 · Cited by 31

Artificial intelligence-aided nanoplasmonic biosensor modeling

Hamedi, S., Jahromi, H. D., & Lotfiani, A. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105646 · Cited by 31

Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction

Vaid, A., Argulian, E., Lerakis, S., Beaulieu-Jones, B. K., Krittanawong, C., Klang, E., Lampert, J., Reddy, V. Y., Narula, J., Nadkarni, G. N., et al. (2023) · Communications Medicine · doi:10.1038/s43856-023-00240-w · Cited by 30

Development of Interpretable Machine Learning Models to Predict in-Hospital Prognosis of Acute Heart Failure Patients

Tanaka, M., Kohjitani, H., Yamamoto, E., Morimoto, T., Kato, T., Yaku, H., Inuzuka, Y., Tamaki, Y., Ozasa, N., Seko, Y., et al. (2024) · ESC Heart Failure · doi:10.1002/ehf2.14834 · Cited by 30

Artificial intelligence in pediatric cardiology: taking baby steps in the big world of data

den Eynde, J. V., Kutty, S., Danford, D. A., & Manlhiot, C. (2021) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000927 · Cited by 32

Artificial Intelligence in Cardiovascular Atherosclerosis Imaging

Zhang, J., Han, R., Shao, G., Lv, B., & Sun, K. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12030420 · Cited by 30

Optimizing the dynamic treatment regime of in-hospital warfarin anticoagulation in patients after surgical valve replacement using reinforcement learning

Zeng, J., Shao, J., Lin, S., Zhang, H., Su, X., Lian, X., Zhao, Y., Ji, X., & Zheng, Z. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac088 · Cited by 30

Effect of virtual reality-based mindfulness training model on anxiety, depression, and cancer-related fatigue in ovarian cancer patients during chemotherapy

Mao, W., Chen, W., & Wang, Y. (2024) · Technology and Health Care · doi:10.3233/thc-230735 · Cited by 29

A machine-learning approach to cardiovascular risk prediction in psoriatic arthritis

Navarini, L., Sperti, M., Currado, D., Costa, L., Deriu, M. A., Margiotta, D. P. E., Tasso, M., Scarpa, R., Afeltra, A., & Caso, F. (2019) · Rheumatology · doi:10.1093/rheumatology/kez677 · Cited by 29

Development of novel machine learning model for right ventricular quantification on echocardiography—A multimodality validation study

Beecy, A., Bratt, A. K., Yum, B., Sultana, R., Das, M., Sherifi, I., Devereux, R. B., Weinsaft, J. W., & Kim, J. J. (2020) · Echocardiography · doi:10.1111/echo.14674 · Cited by 29

The Next Frontier in Pediatric Cardiology

Gaffar, S., Gearhart, A. S., & Chang, A. C. (2020) · Pediatric Clinics of North America · doi:10.1016/j.pcl.2020.06.010 · Cited by 30

Applications of Artificial Intelligence in Non-cardiac Vascular Diseases: A Bibliographic Analysis

Lareyre, F., Le, D., Ballaith, A., Adam, C., Carrier, M., Amrani, S., Caradu, C., & Raffort, J. (2022) · Angiology · doi:10.1177/00033197211062280 · Cited by 29

Artificial intelligence in melanoma: A systematic review

Zhang, S., Wang, Y., Zheng, Q., Li, J., Huang, J., & Long, X. (2022) · Journal of Cosmetic Dermatology · doi:10.1111/jocd.15323 · Cited by 30

Artificial intelligence in cardiac computed tomography

Aromiwura, A. A., Settle, T., Umer, M., Joshi, J. K., Shotwell, M., Mattumpuram, J., Vorla, M., Sztukowska, M. N., Contractor, S. G., Amini, A. A., et al. (2023) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2023.09.001 · Cited by 29

Early prediction of cardiovascular disease using machine learning: Unveiling risk factors from health records

Deepa, R., Sadu, V. B., C, P. G., & Sivasamy, A. (2024) · AIP Advances · doi:10.1063/5.0191990 · Cited by 30

Current Challenges and Recent Updates in Artificial Intelligence and Echocardiography

Gahungu, N., Trueick, R., Bhat, S., Sengupta, P. P., & Dwivedi, G. (2020) · Current Cardiovascular Imaging Reports · doi:10.1007/s12410-020-9529-x · Cited by 28

Early diagnosis of chemotherapy-induced cardiotoxicity by cardiac MRI

Cau, R., Bassareo, P. P., Cherchi, V., Palmisano, V., Suri, J. S., Porcu, M., Balestrieri, A., Pontone, G., & Saba, L. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109158 · Cited by 28

Artificial intelligence interventions focused on opioid use disorders: A review of the gray literature

Beaulieu, T., Knight, R., Nolan, S., Quick, O., & Ti, L. (2020) · The American Journal of Drug and Alcohol Abuse · doi:10.1080/00952990.2020.1817466 · Cited by 28

The Use of Machine Learning for the Care of Hypertension and Heart Failure

Cai, A., Zhu, Y., Clarkson, S. A., & Feng, Y. (2021) · JACC Asia · doi:10.1016/j.jacasi.2021.07.005 · Cited by 28

The Role of Behavioral Economics in Improving Cardiovascular Health Behaviors and Outcomes

Hare, A. J., Patel, M. S., Volpp, K. G. M., & Adusumalli, S. (2021) · Current Cardiology Reports · doi:10.1007/s11886-021-01584-2 · Cited by 28

Construction of Chinese Language Teaching System Model Based on Deep Learning under the Background of Artificial Intelligence

Kang, B., & Kang, S. (2022) · Scientific Programming · doi:10.1155/2022/3960023 · Cited by 28

Tai Chi increases functional connectivity and decreases chronic fatigue syndrome: A pilot intervention study with machine learning and fMRI analysis

Wu, K., Li, Y., Zou, Y., Ren, Y. L., Wang, Y., Hu, X., Wang, Y., Chen, C., Lu, M., Xu, L., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0278415 · Cited by 28

Artificial Intelligence in Heart Failure: Friend or Foe?

Bourazana, A., Xanthopoulos, A. V., Briasoulis, Α., Magouliotis, D. E., Spiliopoulos, K., Athanasiou, T., Vassilopoulos, G., Skoularigis, J. S., & Triposkiadis, F. K. (2024) · Life · doi:10.3390/life14010145 · Cited by 28

Putting AI at the Centre of Heart Failure Care

Ski, C. F., Thompson, D. R., & Rocca, H. B. (2020) · ESC Heart Failure · doi:10.1002/ehf2.12813 · Cited by 29

Leveraging Healthcare System with Nature-Inspired Computing Techniques: An Overview and Future Perspective

Siddiqui, M. F., Alam, A., Калматович, К. Р., Mouna, A., Villela, R., Nurmamatovna, M. A., Mrad, Y. N., Rahat, S. A. A., Magarde, B. K., Muhammad, W., et al. (2022) · Studies in computational intelligence · doi:10.1007/978-981-19-6379-7_2 · Cited by 27

The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis

Reza-Soltani, S., Alam, L. F., Debellotte, O., Monga, T. S., Coyalkar, V. R., Tarnate, V. C. A., Ozoalor, C. U., Allam, S. R., Afzal, M., Shah, G. K., et al. (2024) · Cureus · doi:10.7759/cureus.68472 · Cited by 27

An artificial intelligence-driven revolution in orthopedic surgery and sports medicine

Guan, J., Li, Z., Sheng, S., Lin, Q., Wang, S., Wang, D., Chen, X., & Su, J. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000002187 · Cited by 29

Artificial Intelligence in Diagnosis of Heart Failure

Xie, Y., Zhang, L., Sun, W., Zhu, Y. C., Zhang, Z., Chen, L., Xie, M., & Zhang, L. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.039511 · Cited by 30

Building Computational Models to Predict One-Year Mortality in ICU Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome

Barrett, L. A., Payrovnaziri, S. N., Bian, J., & He, Z. (2018) · AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · doi:10.48550/arxiv.1812.05072 · Cited by 36

Deep Learning in Personalization of Cardiovascular Stents

Lee, Y., Veerubhotla, K., Jeong, M. H., & Lee, C. H. (2019) · Journal of Cardiovascular Pharmacology and Therapeutics · doi:10.1177/1074248419878405 · Cited by 27

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