Artificial Intelligence in Cardiology

Deep learning

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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.

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Mastering the game of Go without human knowledge

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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

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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

Remote patient monitoring using artificial intelligence

Jeddi, Z., & Bohr, A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818438-7.00009-5 · Cited by 90

Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation

Puyol-Antón, E., Ruijsink, B., Mariscal Harana, J., Piechnik, S. K., Neubauer, S., Petersen, S. E., Razavi, R., Chowienczyk, P., & King, A. P. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.859310 · Cited by 85

Sex Disparities in Myocardial Infarction: Biology or Bias?

Stehli, J., Duffy, S. J., Burgess, S. N., Kühn, L., Gulati, M., Chow, C. K., & Zaman, S. J. (2020) · Heart Lung and Circulation · doi:10.1016/j.hlc.2020.06.025 · Cited by 88

AI and machine learning ethics, law, diversity, and global impact

Drabiak, K., Kyzer, S., Nemov, V., & El Naqa, I. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20220934 · Cited by 84

Using gait analysis’ parameters to classify Parkinsonism: A data mining approach

Ricciardi, C., Amboni, M., Santis, C. D., Improta, G., Volpe, G., Iuppariello, L., Ricciardelli, G., D’Addio, G., Vitale, C., Barone, P., et al. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105033 · Cited by 84

Towards a personalised approach in exercise-based cardiovascular rehabilitation: How can translational research help? A ‘call to action’ from the Section on Secondary Prevention and Cardiac Rehabilitation of the European Association of Preventive Cardiology

Gevaert, A. B., Adams, V., Bahls, M., Bowen, T. S., Cornelissen, V., Dörr, M., Hansen, D., Kemps, H., Leeson, P., Craenenbroeck, E. M. V., et al. (2019) · European Journal of Preventive Cardiology · doi:10.1177/2047487319877716 · Cited by 85

The Application of Artificial Intelligence in the Genetic Study of Alzheimer’s Disease

Mishra, R., & Li, B. (2020) · Aging and Disease · doi:10.14336/ad.2020.0312 · Cited by 85

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review

Lopez, C. D., Gazgalis, A., Boddapati, V., Shah, R. P., Cooper, H. J., & Geller, J. A. (2021) · Arthroplasty Today · doi:10.1016/j.artd.2021.07.012 · Cited by 83

Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve

Visco, V., Izzo, C., Mancusi, C., Rispoli, A., Tedeschi, M., Virtuoso, N., Giano, A., Gioia, R., Melfi, A., Serio, B., et al. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10020074 · Cited by 81

Artificial Intelligence in Cardiovascular Medicine

Seetharam, K., Shrestha, S., & Sengupta, P. P. (2019) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-019-0728-1 · Cited by 76

Artificial Intelligence in Multiphoton Tomography: Atopic Dermatitis Diagnosis

Guimarães, P., Batista, A., Zieger, M., Kaatz, M., & Koenig, K. (2020) · Scientific Reports · doi:10.1038/s41598-020-64937-x · Cited by 75

A cardiologist’s guide to machine learning in cardiovascular disease prognosis prediction

Kresoja, K., Unterhuber, M., Wachter, R., Thiele, H., & Lurz, P. C. (2023) · Basic Research in Cardiology · doi:10.1007/s00395-023-00982-7 · Cited by 79

Artificial Intelligence in Modern Medicine – The Evolving Necessity of the Present and Role in Transforming the Future of Medical Care

Bhattad, P. B., & Jain, V. K. (2020) · Cureus · doi:10.7759/cureus.8041 · Cited by 73

Beyond ChatGPT: What does GPT-4 add to healthcare? The dawn of a new era

Wójcik, S., Rulkiewicz, A., Pruszczyk, P., Lisik, W., Poboży, M., & Domienik‐Karłowicz, J. (2023) · Cardiology Journal · doi:10.5603/cj.97515 · Cited by 74

An artificial neural network for the prediction of assisted reproduction outcome

Vogiatzi, P., Pouliakis, A., & Siristatidis, C. S. (2019) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-019-01498-7 · Cited by 73

E-Health in Hypertension Management: an Insight into the Current and Future Role of Blood Pressure Telemonitoring

Omboni, S., Panzeri, E., & Campolo, L. (2020) · Current Hypertension Reports · doi:10.1007/s11906-020-01056-y · Cited by 74

Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: a systematic review with recommendations

Friedrich, S., Groß, S., König, I. R., Engelhardt, S., Bahls, M., Heinz, J., Huber, C., Kaderali, L., Kelm, M., Leha, A., et al. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab054 · Cited by 71

Artificial intelligence in perioperative medicine: a narrative review

Yoon, H., Yang, H.-L., Jung, C.-W., & Lee, H. (2022) · Korean Journal of Anesthesiology · doi:10.4097/kja.22157 · Cited by 73

Utility of machine learning algorithms in assessing patients with a systemic right ventricle

Diller, G., Babu‐Narayan, S. V., Li, W., Radojevic, J., Kempny, A., Uebing, A. S., Dimopoulos, K., Baumgartner, H., Gatzoulis, M. Α., & Orwat, S. (2018) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jey211 · Cited by 70

Sex Differences in Prehospital Delays in Patients With ST‐Segment–Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention

Stehli, J., Dinh, D. T., Dagan, M., Duffy, S. J., Brennan, A., Smith, K. L., Andrew, E., Nehme, Z., Reid, C. M., Lefkovits, J., et al. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.120.019938 · Cited by 69

Artificial Intelligence: Review of Current and Future Applications in Medicine

Thomas, L. B. (2021) · Federal Practitioner · doi:10.12788/fp.0174 · Cited by 72

New Concepts in Sudden Cardiac Arrest to Address an Intractable Epidemic

Narayan, S. M., Wang, P. J., & Daubert, J. P. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.09.083 · Cited by 69

Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses

Liu, A. C., Patel, K., Vunikili, R., Johnson, K. W., Abdu, F., Belman, S. K., Glicksberg, B. S., Tandale, P. P., Fontanez, R., Mathew, O. K., et al. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz059 · Cited by 71

Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification

Prabhakararao, E., & Dandapat, S. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3138986 · Cited by 71

Steps to use artificial intelligence in echocardiography

Kusunose, K. (2020) · Journal of Echocardiography · doi:10.1007/s12574-020-00496-4 · Cited by 67

Machine Learning na Medicina: Revisão e Aplicabilidade

Paixao, G. M., Santos, B. C., de Araujo, R. M., Ribeiro, M. H., de Moraes, J. L., & Ribeiro, A. L. P. (2022) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20200596 · Cited by 65

Artificial Intelligence in Congenital Heart Disease

Jone, P., Gearhart, A. S., Lei, H., Xing, F., Nahar, J. K., López-Jiménez, F. A., Diller, G., Marelli, A. J., Wilson, L., Saidi, A. S., et al. (2022) · JACC Advances · doi:10.1016/j.jacadv.2022.100153 · Cited by 69

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

Burlacu, A., Iftene, A., Jugrin, D., Popa, I. V., Lupu, P. M., Vlad, C.-E., & Covic, A. C. (2020) · BioMed Research International · doi:10.1155/2020/9867872 · Cited by 67

Machine Learning Improves Cardiovascular Risk Definition for Young, Asymptomatic Individuals

Sánchez‐Cabo, F., Rosselló, X., Fuster, V., Benito, F., Manzano, J. P., Silla-Castro, J. C., Fernández-Alvira, J. M., Oliva, B., Fernández‐Friera, L., López‐Melgar, B., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.08.017 · Cited by 67

The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases

Mohsin, S. N., Gapizov, A., Ekhator, C., Ain, N. U., Ahmad, S., Khan, M., Barker, C., Hussain, M., Malineni, J., Ramadhan, A., et al. (2023) · Cureus · doi:10.7759/cureus.44374 · Cited by 65

Fundamentals in Artificial Intelligence for Vascular Surgeons

Raffort, J., Adam, C., Carrier, M., & Lareyre, F. (2019) · Annals of Vascular Surgery · doi:10.1016/j.avsg.2019.11.037 · Cited by 66

Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine

Mohsen, F., Al-Saadi, B., Abdi, N., Khan, S., & Shah, Z. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13081268 · Cited by 64

Artificial Intelligence in Cardiovascular Imaging

Lim, L. J., Tison, G. H., & Delling, F. N. (2020) · Methodist DeBakey Cardiovascular Journal · doi:10.14797/mdcj-16-2-138 · Cited by 63

The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review

Chu, H., Moon, S., Park, J., Bak, S., Ko, Y., & Youn, B.-Y. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.826044 · Cited by 63

Clinlabomics: leveraging clinical laboratory data by data mining strategies

Wen, X., Leng, P., Wang, J., Yang, G., Zu, R., Jia, X., Zhang, K., Mengesha, B. A., Huang, J. H., Wang, D., et al. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04926-1 · Cited by 63

Artificial Intelligence Technologies in Cardiology

Ledziński, Ł., & Grześk, G. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10050202 · Cited by 72

Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future

Yoon, M., Park, J. J., Hur, T., Hua, C.-H., Hussain, M., Lee, S., & Choi, D. (2023) · International Journal of Heart Failure · doi:10.36628/ijhf.2023.0050 · Cited by 66

Do Strategic Human Resources and Artificial Intelligence Help to Make Organisations More Sustainable? Evidence from Non-Governmental Organisations

Alnamrouti, A., Rjoub, H., & Ozgit, H. (2022) · Sustainability · doi:10.3390/su14127327 · Cited by 62

Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study

Laupichler, M. C., Hadizadeh, D. R., Wintergerst, M. W. M., von der Emde, L., Paech, D., Dick, E. A., & Raupach, T. (2022) · BMC Medical Education · doi:10.1186/s12909-022-03866-x · Cited by 62

The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

Seetharam, K., Brito, D., Farjo, P. D., & Sengupta, P. P. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2020.618849 · Cited by 61

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects

Haq, I. U., Chhatwal, K., SANAKA, K. O., & Xu, B. (2022) · Vascular Health and Risk Management · doi:10.2147/vhrm.s279337 · Cited by 65

Artificial intelligence in personalized cardiovascular medicine and cardiovascular imaging

Haq, I.-U., Haq, I., & Xu, B. (2021) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt.2020.03.09 · Cited by 62

Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases

Shu, S., Ren, J., & Song, J. (2021) · Circulation Journal · doi:10.1253/circj.cj-20-1121 · Cited by 60

Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation: comparing common clinical risk scores and machine learning algorithms

Lip, G. Y. H., Tran, G. N., Genaidy, A., Marroquin, P., Estes, C., & Landsheft, J. (2021) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcab037 · Cited by 61

Cardiovascular Disease Prediction by Machine Learning Algorithms Based on Cytokines in Kazakhs of China

Jiang, Y., Zhang, X., Ma, R., Wang, X., Liu, J., Keerman, M., Yan, Y., Ma, J., Song, Y., Zhang, J., et al. (2021) · Clinical Epidemiology · doi:10.2147/clep.s313343 · Cited by 63

Artificial Intelligence and Cardiovascular Genetics

Krittanawong, C., Johnson, K. W., Choi, E., Kaplin, S. L., Venner, E., Murugan, M., Wang, Z., Glicksberg, B. S., Amos, C. I., Schatz, M. C., et al. (2022) · Life · doi:10.3390/life12020279 · Cited by 59

Machine intelligence in cardiovascular medicine

Miller, D. D. (2020) · Cardiology in Review · doi:10.1097/crd.0000000000000294 · Cited by 58

Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography

Martinez, D. S.-L., Noseworthy, P. A., Akbilgiç, O., Herrmann, J., Ruddy, K. J., Hamid, A. B., Maddula, R., Singh, A., Davis, R. L., Güntürkün, F., et al. (2022) · American Heart Journal Plus Cardiology Research and Practice · doi:10.1016/j.ahjo.2022.100129 · Cited by 58

Role of artificial intelligence and machine learning in ophthalmology

Akkara, J., & Kuriakose, A. (2019) · Kerala Journal of Ophthalmology · doi:10.4103/kjo.kjo_54_19 · Cited by 58

Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges

Doolub, G., Khurshid, S. S., Thériault-Lauzier, P., Lapalme, A. N., Tastet, O., So, D. Y. F., Langlais, É. L., Corbin, D., & Avram, R. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.06.011 · Cited by 33

Reflecting health: smart mirrors for personalized medicine

Miotto, R., Danieletto, M., Scelza, J., Kidd, B. A., & Dudley, J. T. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0068-7 · Cited by 59

Artificial intelligence: improving the efficiency of cardiovascular imaging

Lin, A., Kolossváry, M., Išgum, I., Maurovich‐Horvat, P., Slomka, P. J., & Dey, D. (2020) · Expert Review of Medical Devices · doi:10.1080/17434440.2020.1777855 · Cited by 59

Cardiac CT angiography in current practice: An American society for preventive cardiology clinical practice statement✰

Budoff, M. J., Lakshmanan, S., Toth, P. P., Hecht, H. S., Shaw, L. J., Maron, D. J., Michos, E. D., Williams, K. A., Sr., Nasir, K., Choi, A., et al. (2022) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2022.100318 · Cited by 54

Artificial intelligence in dermatology and healthcare: An overview

Pai, V. V., & Pai, R. B. (2021) · Indian Journal of Dermatology Venereology and Leprology · doi:10.25259/ijdvl_518_19 · Cited by 57

Estimation of Muscle Mass Using Creatinine/Cystatin C Ratio in Japanese Community-Dwelling Older People

Kusunoki, H., Tabara, Y., Tsuji, S., Wada, Y., Tamaki, K., Nagai, K., Itoh, M., Sano, K., Amano, M., Maeda, H., et al. (2021) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2021.07.029 · Cited by 54

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Sethi, Y., Patel, N., Kaka, N., Desai, A., Kaiwan, O., Sheth, M., Sharma, R., Huang, H. Y. R., Chopra, H., Khandaker, M. U., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11237072 · Cited by 55

Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review

den Eynde, J. V., Lachmann, M., Laugwitz, K., Manlhiot, C., & Kutty, S. (2022) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2022.01.010 · Cited by 57

The Role of Artificial Intelligence in Echocardiography

Seetharam, K., Raina, S., & Sengupta, P. P. (2020) · Current Cardiology Reports · doi:10.1007/s11886-020-01329-7 · Cited by 52

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Alamgir, A., Mousa, O., & Shah, Z. (2021) · JMIR Medical Informatics · doi:10.2196/30798 · Cited by 54

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

Yaseen, I., & Rather, R. A. (2024) · International Journal of Women s Health · doi:10.2147/ijwh.s454127 · Cited by 51

Orthodontics in the era of big data analytics

Allareddy, V., Venugopalan, S. R., Nalliah, R. P., Caplin, J., Lee, M. K., & Allareddy, V. (2019) · Orthodontics and Craniofacial Research · doi:10.1111/ocr.12279 · Cited by 54

Advancing Research on the Complex Interrelations Between Atrial Fibrillation and Heart Failure

Al‐Khatib, S. M., Benjamin, E. J., Albert, C. M., Alonso, Á., Chauhan, C., Chen, P., Curtis, A. B., Desvigne-Nickens, P. M., Ho, J. E., Lam, C. S. P., et al. (2020) · Circulation · doi:10.1161/circulationaha.119.045204 · Cited by 53

Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management

Fu, Z., Hong, S., Zhang, R., & Du, S. (2021) · Sensors · doi:10.3390/s21030773 · Cited by 53

Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

Lorenzoni, G., Sabato, S. S., Lanera, C., Bottigliengo, D., Minto, C., Ocagli, H., Paolis, P. D., Gregori, D., Iliceto, S., & Pisanò, F. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8091298 · Cited by 53

Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI

Ocasio, E., & Duong, T. Q. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.560 · Cited by 51

A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients

Arezzo, F., Cormio, G., Forgia, D. L., Santarsiero, C. M., Mongelli, M., Lombardi, C. M., Cazzato, G., Cicinelli, E., & Loizzi, V. (2022) · Archives of Gynecology and Obstetrics · doi:10.1007/s00404-022-06578-1 · Cited by 52

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Stamate, E., Piraianu, A. I., Ciobotaru, O. R., Crassas, R., Duca, O.-M., Fulga, A., Grigore, I., Vintila, V., Fulga, I., & Ciobotaru, O. C. (2024) · Diagnostics · doi:10.3390/diagnostics14111103 · Cited by 49

The Application of Artificial Intelligence Technology in Healthcare: A Systematic Review

Alloghani, M. A., Al‐Jumeily, D., Aljaaf, A. J., Khalaf, M., Mustafina, J., & Tan, S. Y. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-38752-5_20 · Cited by 49

Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques

Hussain, A., Choi, H. E., Kim, H.-J., Aich, S., Saqlain, M., & Kim, H.-C. (2021) · Diagnostics · doi:10.3390/diagnostics11050829 · Cited by 49

Multi-omics data integration approaches for precision oncology

Aguila, R. C., Alonso-Pupo, N., & Hernández‐Rodríguez, E. W. (2022) · Molecular Omics · doi:10.1039/d1mo00411e · Cited by 52

Recent advances of artificial intelligence in quantitative analysis of food quality and safety indicators: A review

Yi, L., Wang, W., Diao, Y., Yi, S., Shang, Y., Ren, D., Ge, K., & Gu, Y. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.117944 · Cited by 50

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Velázquez‐Díaz, D., Arco, J. E., Ortíz, A., Pérez‐Cabezas, V., Lucena-Antón, D., Moral-Muñoz, J. A., & Galán-Mercant, A. (2023) · Journal of Medical Internet Research · doi:10.2196/47346 · Cited by 47

Guidelines os the Brazilian Society of Cardiology on Telemedicine in Cardiology - 2019

Lopes, M. A. C. Q., de Oliveira, G. M. M., Ribeiro, A. L. P., Pinto, F. J., Rey, H. C. V., o, A. ́. A. ́. B., Zimerman, L. I., Hajjar, L. A., Rochitte, C. E., Bacal, F., et al. (2019) · Arquivos Brasileiros de Cardiologia · doi:10.5935/abc.20190205 · Cited by 50

Artificial intelligence and the future of life sciences

Leite, M. L., Costa, L. S. D. L., Cunha, V. A., Kreniski, V., Filho, M. D. O. B., da Cunha, N. B., & Costa, F. F. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.07.002 · Cited by 48

Trust in medical artificial intelligence: a discretionary account

Nickel, P. J. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09630-5 · Cited by 50

Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease

Lu, H., Yao, Y., Wang, L., Yan, J., Tu, S., Xie, Y., & He, W. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/3016532 · Cited by 45

Artificial Intelligence in Echocardiography: The Time is Now

Sehly, A. A., Jaltotage, B., He, A., Maiorana, A., Ihdayhid, A. R., Rajwani, A., & Dwivedi, G. (2022) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2308256 · Cited by 45

Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review

Nazir, S., Nawaz, M., Adnan, A., Shahzad, S., & Asadi, S. (2019) · IEEE Access · doi:10.1109/access.2019.2941898 · Cited by 45

Research on digital technology use in cardiology: bibliometric analysis

Yeung, A. W. K., Kulnik, S. T., Parvanov, E. D., Fassl, A., Eibensteiner, F., Völkl-Kernstock, S., Kletecka-Pulker, M., Crutzen, R., Gutenberg, J., Höppchen, I., et al. (2022) · Journal of Medical Internet Research · doi:10.2196/36086 · Cited by 44

Applications of Machine Learning in Cardiology

Seetharam, K., Balla, S., Bianco, C. M., Cheung, J. W., PACHULSKI, R. T., Asti, D., Nalluri, N., Tejpal, A., Mir, P., Shah, J. M., et al. (2022) · Cardiology and Therapy · doi:10.1007/s40119-022-00273-7 · Cited by 45

Artificial intelligence and machine learning in cardiotocography: A scoping review

Aeberhard, J. L., Radan, A., Delgado-Gonzalo, R., Strahm, K. M., Sigurthorsdottir, H., Schneider, S., & Surbek, D. (2022) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2022.12.008 · Cited by 48

Statistical Tests for Replacing Human Decision Makers with Algorithms

Feng, K., HONG, H. P., Tang, K., & Wang, J. (2025) · Management Science · doi:10.1287/mnsc.2023.01845 · Cited by 50

Inteligência Artificial em Cardiologia: Conceitos, Ferramentas e Desafios – “Quem Corre é o Cavalo, Você Precisa ser o Jóquei”

Filho, E. M. D. S., Fernandes, F. D. A., Soares, C. L. D. A., Seixas, F. L., dos Santos, A. A. S. M. D., Gismondi, R. A., Mesquita, E. T., & Mesquita, C. T. (2019) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20180431 · Cited by 47

Identifying Heart Failure in ECG Data With Artificial Intelligence—A Meta-Analysis

Grün, D., Rudolph, F., Gumpfer, N., Hannig, J., Elsner, L. K., von Jeinsen, B., Hamm, C. W., Rieth, A. J., Guckert, M., & Keller, T. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2020.584555 · Cited by 46

Evolution of single-lead ECG for STEMI detection using a deep learning approach

Gibson, C. M., Mehta, S., Ceschim, M. R. S., Frauenfelder, A., Vieira, D., Botelho, R., Fernandez, F., Villagran, C., Niklitschek, S., Matheus, C., et al. (2021) · International Journal of Cardiology · doi:10.1016/j.ijcard.2021.11.039 · Cited by 44

A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology

Manlhiot, C., den Eynde, J. V., Kutty, S., & Ross, H. J. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.11.009 · Cited by 47

Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector

Shaikh, A. K., Alhashmi, S. M., Khalique, N., Khedr, A. M., Raahemifar, K., & Bukhari, S. (2023) · Digital Health · doi:10.1177/20552076221149296 · Cited by 44

Artificial intelligence in healthcare: a mastery

Mukherjee, J., Sharma, R., Dutta, P., & Bhunia, B. (2023) · Biotechnology and Genetic Engineering Reviews · doi:10.1080/02648725.2023.2196476 · Cited by 45

Ai-driven Predictive Analytics, Healthcare Outcomes, Cost Reduction, Machine Learning, Patient Monitoring

Hossain, S., Hossain, S., -, U. K., -, S. S., & -, N. K. (2024) · Advanced International Journal of Multidisciplinary Research · doi:10.62127/aijmr.2024.v02i05.1104 · Cited by 50

Sex Disparities in Cardiovascular Device Evaluations

Ghare, M. I., Chandrasekhar, J., Mehran, R., Ng, V. G., Grines, C. L., & Lansky, A. J. (2019) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2018.10.048 · Cited by 43

Distinguishing Functional from Non-functional Pituitary Macroadenomas with a Machine Learning Analysis

Ricciardi, C., Cuocolo, R., Cesarelli, G., Ugga, L., Improta, G., Solari, D., Romeo, V., Guadagno, E., Maria, C. L., & Cesarelli, M. (2019) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-030-31635-8_221 · Cited by 43

Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics

Poduval, M., Ghose, A., Manchanda, S., Bagaria, V. B., & Sinha, A. (2020) · Indian Journal of Orthopaedics · doi:10.1007/s43465-019-00023-3 · Cited by 44

Liquid biopsy technologies for hematological diseases

Fu, Y., Zhang, Y., & Khoo, B. L. (2020) · Medicinal Research Reviews · doi:10.1002/med.21731 · Cited by 46

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Gautam, N., Saluja, P., Malkawi, A., Rabbat, M., Al-Mallah, M. H., Pontone, G., Zhang, Y., Lee, B. C., & Alaref, S. J. (2022) · Healthcare · doi:10.3390/healthcare10020232 · Cited by 44

Building an Otoscopic screening prototype tool using deep learning

Livingstone, D., Talai, A. S., Chau, J., & Forkert, N. D. (2019) · Journal of Otolaryngology - Head & Neck Surgery · doi:10.1186/s40463-019-0389-9 · Cited by 42

Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation

Lü, J., Hutchens, R., Hung, J., Bennamoun, M., McQuillan, B., Briffa, T. G., Sohel, F. A., Murray, K., Stewart, J. E., Chow, B. J. W., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106126 · Cited by 43

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Xu, Y., Cao, L., Chen, Y., Zhang, Z., Liu, W., Li, H., Ding, C., Pu, J., Qian, K., & Xu, W. L. (2024) · Small Methods · doi:10.1002/smtd.202400305 · Cited by 42

Artificial Intelligence in Sports Medicine: Reshaping Electrocardiogram Analysis for Athlete Safety—A Narrative Review

Smaranda, A. M., Drăgoiu, T. S., Caramoci, A., Afetelor, A. A., Ionescu, A. M., & Bădărău, I. A. (2024) · Sports · doi:10.3390/sports12060144 · Cited by 46

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