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.

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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

The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review

Dimopoulos, P. M., Mulita, A., Antzoulas, A., Bodard, S., Leivaditis, V., Ακρίδα, Ι., Benetatos, N., Katsanos, K., Anagnostopoulos, C., & Mulita, F. (2024) · Gastroenterology Review · doi:10.5114/pg.2024.143147 · Cited by 18

Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data

Khorsand, B., Rajabnia, M., Jahanian, A., Fathy, M., Taghvaei, S., & Houri, H. (2024) · Advances in Medical Sciences · doi:10.1016/j.advms.2024.10.001 · Cited by 20

Long-term PM2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation

Kim, I., Yang, P., Jang, E., Jung, H., You, S. C., Yu, H. T., Kim, T., Uhm, J., Pak, H., Lee, M., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-73537-8 · Cited by 18

Application of Artificial Intelligence in the Diagnosis and Management of Corneal Diseases

Tahvildari, M., Singh, R. B., & Saeed, H. N. (2021) · Seminars in Ophthalmology · doi:10.1080/08820538.2021.1893763 · Cited by 19

Role of artificial intelligence in cardiology

Vidal-Pérez, R., & Vázquez‐Rodríguez, J. M. (2023) · World Journal of Cardiology · doi:10.4330/wjc.v15.i4.116 · Cited by 18

Towards Autonomous Healthcare: Integrating Artificial Intelligence (AI) for Personalized Medicine and Disease PredictionPreprint

Rane, N. L., Choudhary, S., & Rane, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4637894 · Cited by 19

Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports

Dewaswala, N., Chen, D., Bhopalwala, H., Kaggal, V. C., Murphy, S. P., Bos, J. M., Geske, J. B., Gersh, B. J., Ommen, S. R., Araoz, P. A., et al. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02017-y · Cited by 17

Information Technologies of the Processing of the Spaces of the States of a Complex Biophysical Object in the Intellectual Medical System HEALTH

Babker, A. M. A. (2019) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2019/89862019 · Cited by 17

Clinical applications of artificial intelligence in cardiology on the verge of the decade

Pieszko, K., Hiczkiewicz, J., Budzianowski, J., Musielak, B., Hiczkiewicz, D., Faron, W., Rzeźniczak, J., & Burchardt, P. (2020) · Cardiology Journal · doi:10.5603/cj.a2020.0093 · Cited by 18

Digital technologies and the democratization of clinical research: Social media, wearables, and artificial intelligence

Korjian, S., & Gibson, C. M. (2022) · Contemporary Clinical Trials · doi:10.1016/j.cct.2022.106767 · Cited by 17

Assessing electrocardiogram changes after ischemic stroke with artificial intelligence

Zeng, Z., Wang, Q., Yu, Y., Zhang, Y., Chen, Q., Lou, W., Wang, Y., Yan, L., Cheng, Z., Xu, L., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0279706 · Cited by 17

Artificial intelligence, big data and machine learning approaches in genome-wide SNP-based prediction for precision medicine and drug discovery

Joshi, I., Bhrdwaj, A., Khandelwal, R., Pande, A., Agarwal, A., Srija, C. D., Suresh, R. A., Mohan, M., Hazarika, L., Thakur, G., et al. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85713-0.00021-9 · Cited by 17

Artificial intelligence and its application for cardiovascular diseases in Chinese medicine

Chen, X., LEUNG, Y.-L. A., & Jiangang, S. H. E. N. (2022) · Digital Chinese Medicine · doi:10.1016/j.dcmed.2022.12.003 · Cited by 17

Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence

Rech, M. M., Filho, L. D. M., White, A. J., Perez‐Vega, C., Samson, S. L., Chaichana, K. L., Olomu, O. U., Quiñones‐Hinojosa, A., & Almeida, J. P. (2023) · Brain Sciences · doi:10.3390/brainsci13030495 · Cited by 18

Current role and future perspectives of artificial intelligence in echocardiography

Vidal-Pérez, R., Grapsa, J., Bouzas‐Mosquera, A., Fontes‐Carvalho, R., & Vázquez‐Rodríguez, J. M. (2023) · World Journal of Cardiology · doi:10.4330/wjc.v15.i6.284 · Cited by 18

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine

Salimi, M., Roshanfar, M., Tabatabaei, N., & Mosadegh, B. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm14010033 · Cited by 17

Prediction of coronary artery disease based on facial temperature information captured by non-contact infrared thermography

Kung, M., Zeng, J., Lin, S., Yu, X., Liu, C., Shi, M., Sun, R., Yuan, S., Lian, X., Su, X., et al. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100942 · Cited by 17

Artificial intelligence in fracture detection on radiographs: a literature review

Mastro, A. L., Grassi, E., Berritto, D., Russo, A., Reginelli, A., Guerra, E., Grassi, F., & Boccia, F. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-024-01702-4 · Cited by 18

Toward Precision Health: Applying Artificial Intelligence Analytics to Digital Health Biometric Datasets

Cohoon, T. J., & Bhavnani, S. P. (2020) · Personalized Medicine · doi:10.2217/pme-2019-0113 · Cited by 17

Accuracy and Interpretability: Struggling with the Epistemic Foundations of Machine Learning-Generated Medical Information and Their Practical Implications for the Doctor-Patient Relationship

Funer, F. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00505-7 · Cited by 18

Artificial intelligence in cardiology: applications, benefits and challenges

Constantinides, P., & Fitzmaurice, D. (2018) · British Journal of Cardiology · doi:10.5837/bjc.2018.024 · Cited by 17

Multicenter Analysis of Dosing Protocols for Sotalol Initiation

Biswas, M., Levy, A. E., Weber, R. A., Tarakji, K. G., Chung, M. K., Noseworthy, P. A., Newton‐Cheh, C., & Rosenberg, M. A. (2019) · Journal of Cardiovascular Pharmacology and Therapeutics · doi:10.1177/1074248419887710 · Cited by 17

Recent advances in applications of machine learning in cervical cancer research: a focus on prediction models

Abrar, S. S., Isa, S. A. M., Hairon, S. M., Ismail, M. P., & Kadir, M. N. N. A. (2025) · Obstetrics & Gynecology Science · doi:10.5468/ogs.25041 · Cited by 16

Artificial Intelligence in Cardiovascular Imaging: Current Applications and New Horizons

Baggiano, A., Mushtaq, S., Fusini, L., Muratori, M., Pontone, G., & Pepi, M. (2025) · Journal of Cardiovascular Echography · doi:10.4103/jcecho.jcecho_62_25 · Cited by 18

Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling

Ma, W., Li, H., Dong, L., Zhou, Q., Fu, B., Hou, J., Wang, J., Qin, W., & Chen, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-93317-2 · Cited by 16

Identification of hub genes and their correlation with immune infiltration in coronary artery disease through bioinformatics and machine learning methods

Huang, K., Zheng, H.-L., Li, S., & Zeng, Z. (2022) · Journal of Thoracic Disease · doi:10.21037/jtd-22-632 · Cited by 16

Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy

Arezzo, F., Cormio, G., Mongelli, M., Cazzato, G., Silvestris, E., Kardhashi, A., Cazzolla, A., Lombardi, C. M., Venerito, V., & Loizzi, V. (2022) · Archives of Gynecology and Obstetrics · doi:10.1007/s00404-022-06824-6 · Cited by 18

Differential Beat Accuracy for ECG Family Classification Using Machine Learning

Vadillo-Valderrama, A., Goya–Esteban, R., Caulier‐Cisterna, R., Garcı́a-Alberola, A., & Rojo‐Álvarez, J. L. (2022) · IEEE Access · doi:10.1109/access.2022.3227219 · Cited by 16

Machine Learning for Prediction of Cardiovascular Disease and Respiratory Disease: A Review

Parashar, G., Chaudhary, A., & Pandey, D. (2024) · SN Computer Science · doi:10.1007/s42979-023-02529-y · Cited by 17

Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES

Zhang, Y., Li, S., Wu, W., Zhao, Y., Han, J., Tong, C., Luo, N., & Zhang, K. (2024) · BioData Mining · doi:10.1186/s13040-024-00363-3 · Cited by 16

Potentials of artificial intelligence in familial hypercholesterolemia: Advances in screening, diagnosis, and risk stratification for early intervention and treatment

Athar, M. (2024) · International Journal of Cardiology · doi:10.1016/j.ijcard.2024.132315 · Cited by 16

A Head-to Head Comparison of Machine Learning Algorithms for Identification of Implanted Cardiac Devices

Chudow, J., Jones, D. B., Weinreich, M., Zaremski, L., Lee, S. K., Weinreich, B., KRUMERMAN, A. K., Fisher, J. D., & Ferrick, K. J. (2020) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2020.12.067 · Cited by 16

Adult congenital heart disease: Looking back, moving forward

Ntiloudi, D., Gatzoulis, M. Α., Arvanitaki, A., Karvounis, H. I., & Giannakoulas, G. A. (2021) · International Journal of Cardiology Congenital Heart Disease · doi:10.1016/j.ijcchd.2020.100076 · Cited by 16

Cardiac Diagnosis with Machine Learning: A Paradigm Shift in Cardiac Care

Laad, M. S., Kotecha, K. V., Patil, K. R., & Pise, R. N. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2031816 · Cited by 17

Artificial intelligence applied to cardiovascular imaging, a critical focus on echocardiography: The point‐of‐view from “the other side of the coin”

Dell’Angela, L., & Nicolosi, G. L. (2022) · Journal of Clinical Ultrasound · doi:10.1002/jcu.23215 · Cited by 16

Big Data in cardiac surgery: real world and perspectives

Montisci, A., Palmieri, V., Vietri, M. T., Sala, S., Maiello, C., Donatelli, F., & Claudio Napoli, A. (2022) · Journal of Cardiothoracic Surgery · doi:10.1186/s13019-022-02025-z · Cited by 17

Combined Applications of Artificial Intelligence and Simulation for Healthcare Process Optimization: A Systematic Review

Álvarez-Vázquez, J., Casal-Guisande, M., Fernández-García, A., Mosteiro, M., Torres‐Durán, M., & Fernández‐Villar, A. (2025) · Healthcare · doi:10.3390/healthcare13222933 · Cited by 16

Real-time AI in Clinical Decision Support

Veernapu, K., Patil, B. K., Tharayil, A. S., Sindhura, C. L., Andy, A., Budhewar, A. S., Somwanshi, S. K., Kavya, K., Bharadwaj, M., & Raj, P. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6400-1.ch015 · Cited by 16

Machine learning as a tool for diagnostic and prognostic research in coronary artery disease

Geltser, B. I., Tsivanyuk, M. M., Shakhgeldyan, K. I., & Rublev, V. Y. (2020) · Russian Journal of Cardiology · doi:10.15829/1560-4071-2020-3999 · Cited by 15

Implementation of digital technologies in the medicine of the future

Rakhimov, T. K., & Mukhamediev, M. (2022) · Futurity Medicine. · doi:10.57125/fem.2022.06.30.02 · Cited by 15

Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability

Shan, D., Wang, S., Wang, J., Lü, J., Ren, J., Chen, J., Wang, D., & Qi, P. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1151326 · Cited by 15

AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study

de Koning, E. R., van der Haas, Y. J., Saguna, S., Stoop, E., Bosch, J., Beeres, S. L. M. A., Schalij, M. J., & Boogers, M. J. (2023) · JMIR Cardio · doi:10.2196/51375 · Cited by 16

Pricing Powered by Artificial Intelligence: An Assessment Model for the Sustainable Implementation of AI Supported Price Functions

Erdmann, A., Yazdani, M., Iglesias, J. M. M., & Palacios, C. M. (2024) · Informatica · doi:10.15388/24-infor559 · Cited by 15

Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data

Bhagawati, M., Paul, S., Mantella, L. E., Johri, A. M., Gupta, S., Laird, J. R., Singh, I. M., Khanna, N. N., Al-Maini, M., Isenović, E. R., et al. (2024) · Diagnostics · doi:10.3390/diagnostics14171894 · Cited by 15

SPINE20 Recommendations 2024 -Spinal Disability: Social Inclusion as a Key to Prevention and Management

Menezes, C. M., Tucci, C., Tamai, K., Chhabra, H. S., AlHelal, F. H., Bussières, A., Muehlbauer, E. J., Roberts, L. C., Alsobayel, H., Barneschi, G., et al. (2024) · Global Spine Journal · doi:10.1177/21925682241290226 · Cited by 15

Artificial Intelligence in Fetal and Pediatric Echocardiography

Wang, A., Doan, T. T., Reddy, C. D., & Jone, P. (2024) · Children · doi:10.3390/children12010014 · Cited by 15

Technological advances to enhance recovery after cardiac surgery

Lobdell, K. W., Appoo, J. J., Rose, G. A., Ferguson, B., & Chatterjee, S. (2021) · Journal of Hospital Management and Health Policy · doi:10.21037/jhmhp-20-114 · Cited by 15

Stress Cardiomyopathy in Hospitalized Patients with Cancer: Machine Learning Analysis by Primary Malignancy Type

Javaid, A. I., Monlezun, D. J., Iliescu, G., Tran, P., Filipescu, A. G., Palaskas, N. L., Lopez‐Mattei, J., Hassan, S. A., Kim, P. Y., Madjid, M., et al. (2021) · ESC Heart Failure · doi:10.1002/ehf2.13647 · Cited by 15

Single-cell sequencing combined with machine learning reveals the mechanism of interaction between epilepsy and stress cardiomyopathy

Ji, X., Pei, Q., Zhang, J., Lin, P., Li, B., Yin, H., Sun, J., Su, D., Qu, X., & Yin, D. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1078731 · Cited by 15

Künstliche Intelligenz und potenzielle Anwendungsfelder im Marketing

Richter, A., Gačić, T., Köelmel, B., & Waidelich, L. (2019) · Springer eBooks · doi:10.1007/978-3-658-25583-1_2 · Cited by 15

Leveraging machine learning for enhanced and interpretable risk prediction of venous thromboembolism in acute ischemic stroke care

Jiang, Y., Li, A., Li, Z., Li, Y., Li, R., Zhao, Q., & Li, G. (2025) · PLoS ONE · doi:10.1371/journal.pone.0302676 · Cited by 14

Interpretable machine learning model for cardiovascular disease risk prediction: a feature decomposition-based study

Yu, L., Wu, J.-C., Wu, X., Chen, C., Chen, Y., Fang, L., & Zheng, D. (2025) · BMC Public Health · doi:10.1186/s12889-025-24921-4 · Cited by 14

Hybrid Deep Learning Approach for Automatic Detection in Musculoskeletal Radiographs

Singh, G., Anand, D., Cho, W., Joshi, G. P., & Son, K. C. (2022) · Biology · doi:10.3390/biology11050665 · Cited by 14

Redes neuronales convolucionales: un modelo de Deep Learning en imágenes diagnósticas. Revisión de tema

Badillo, F. L., Hernández, C. A. R., Narváez, B. M., & Estefanía, Y. (2021) · Revista colombiana de radiología · doi:10.53903/01212095.161 · Cited by 24

Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application?

Kim, K., Kwon, J. M., Pereira, T., Attia, Z. I., & Pereira, N. L. (2022) · Current Cardiology Reports · doi:10.1007/s11886-022-01776-4 · Cited by 14

The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review

Tardo, D. T., Peck, M., Subbiah, R. N., Vandenberg, J. I., & Hill, A. P. (2022) · Annals of Noninvasive Electrocardiology · doi:10.1111/anec.13015 · Cited by 15

The Heart and Artificial Intelligence—How Can We Improve Medicine Without Causing Harm

Reich, C., & Meder, B. (2023) · Current Heart Failure Reports · doi:10.1007/s11897-023-00606-0 · Cited by 16

External evaluation of a commercial artificial intelligence-augmented digital auscultation platform in valvular heart disease detection using echocardiography as reference standard

Roquemen-Echeverri, V., Jacobs, P. G., Shalen, E. F., Schulman, P. M., Heitner, S. B., Denfeld, Q. E., Wilson, B., Halvorson, J., Scott, D., Londoño-Murillo, T., et al. (2024) · International Journal of Cardiology · doi:10.1016/j.ijcard.2024.132653 · Cited by 14

Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management

Armoundas, A. A., Ahmad, F. S., Attia, Z. I., Doudesis, D., Khera, R., Kyriakoulis, K. G., Stergiou, G. S., & Tang, W. H. W. (2025) · Hypertension · doi:10.1161/hypertensionaha.124.22349 · Cited by 14

Risk Assessment in Patients with a Left Ventricular Assist Device Across INTERMACS Profiles Using Bayesian Analysis

Kanwar, M. K., Lohmueller, L. C., Teuteberg, J. J., Kormos, R. L., Rogers, J. G., Benza, R. L., Lindenfeld, J. M., McIlvennan, C. K., Bailey, S. H., Murali, S., et al. (2019) · ASAIO Journal · doi:10.1097/mat.0000000000000910 · Cited by 14

AI and IoT in Healthcare

Singla, S. K. (2020) · Springer eBooks · doi:10.1007/978-3-030-37526-3_1 · Cited by 14

Explainable machine learning models for predicting 30-day readmission in pediatric pulmonary hypertension: A multicenter, retrospective study

Duan, M., Shu, T., Zhao, B., Xiang, T., Wang, J., Huang, H., Zhang, Y., Xiao, P., Zhou, B., Xie, Z., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.919224 · Cited by 14

A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging

Johnson, K. W., Glicksberg, B. S., Shameer, K., Vengrenyuk, Y., Krittanawong, C., Russak, A. J., Sharma, S. K., Narula, J., Dudley, J. T., & Kini, A. S. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.05.007 · Cited by 15

Quantification of myocardial blood flow using PET: Current status, clinical applications, and future directions

Vohra, S., Gupta, S., Agarwal, V., & Patel, K. (2025) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2025.09.011 · Cited by 13

Advancing precision medicine in medical education: Integrated, precise and data‐driven smart solutions

Papadopoulou, P., & Lytras, M. D. (2023) · Applied Research · doi:10.1002/appl.202200131 · Cited by 15

Artificial intelligence applied in pulmonary hypertension: a bibliometric analysis

Tchuenté-Foguem, G., & Keleko, A. T. (2023) · AI and Ethics · doi:10.1007/s43681-023-00267-8 · Cited by 13

Presurgery and postsurgery: advancements in artificial intelligence and machine learning models for enhancing patient management in infective endocarditis

Odat, R. M., Marsool, M. D. M., Nguyen, D. H., Idrees, M., Hussein, A. M., Ghabally, M., Yasin, J. A., Hanifa, H., Sabet, C. J., Định, N. H., et al. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000002003 · Cited by 14

The perception of artificial intelligence and infertility care among patients undergoing fertility treatment

Cromack, S. C., Lew, A. M., Bazzetta, S. E., Xu, S., & Walter, J. R. (2025) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-024-03382-5 · Cited by 13

Harnessing Artificial Intelligence in Cardiology: Advancements in Diagnosis, Treatment, and Patient Care

Shuja, M. H., Shakil, F., Shuja, S. H., Hasan, M., Edhi, M., Abbasi, A. F., Jawaid, A., & Shakil, S. (2024) · Heart Views · doi:10.4103/heartviews.heartviews_103_24 · Cited by 14

A narrative review on ethical considerations and challenges in AI-driven cardiology

Patel, D. K., Chetarajupalli, C., Khan, S. S., Khan, S., Patel, T. D., Joshua, S., & Millis, R. M. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000003349 · Cited by 15

Anastomotic Leak is Increased With Clostridium difficile Infection After Colectomy: Machine Learning-Augmented Propensity Score Modified Analysis of 46 735 Patients

Baker, S. E., Monlezun, D. J., Ambroze, W. L., & Margolin, D. A. (2020) · The American Surgeon · doi:10.1177/0003134820973720 · Cited by 13

La inteligencia artificial en la educación médica y la predicción en salud

Joison, A. N., Barcudi, R. J., Majul, E. A., Ruffino, S., J, D. M. R. J., Joison, A. M., & Baiardi, G. (2021) · Methodo Investigación Aplicada a las Ciencias Biológicas · doi:10.22529/me.2021.6(1)07 · Cited by 13

Prediction and classification of diabetes mellitus using genomic data

Awotunde, J. B., Ayo, F. E., Jimoh, R. G., Ogundokun, R. O., Matiluko, O. E., Oladipo, I. D., & AbdulRaheem, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821187-8.00009-5 · Cited by 14

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

Kutiame, S., Millham, R. C., Adekoya, A. F., Tettey, M. M., Weyori, B. A., & Appiahene, P. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130620 · Cited by 13

Fourier space approach for convolutional neural network (CNN) electrocardiogram (ECG) classification: A proof-of-concept study

Kent, M., Vasconcelos, L., Ansari, S., Ghanbari, H., & Nenadic, I. (2023) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2023.04.004 · Cited by 14

IMPACTO DA TECNOLOGIA DE INTELIGÊNCIA ARTIFICIAL NA MEDICINA DIAGNÓSTICA

de Moraes, J. J., Vieira, P. H., Barbosa, M. C. M. D. A., da Costa, A. C. M. D. S. F., Romeiro, E. T., Terebinto, D. V., do Vale, M. D. C., de Almeida, M. O., Pinto, S. P. T., & Zbierski, M. D. L. (2023) · Revista Ibero-Americana de Humanidades, Ciências e Educação · doi:10.51891/rease.v9i7.10699 · Cited by 13

Enhancing Risk Stratification on Coronary Computed Tomography Angiography: The Role of Artificial Intelligence

Jaltotage, B., Sukudom, S., Ihdayhid, A. R., & Dwivedi, G. (2023) · Clinical Therapeutics · doi:10.1016/j.clinthera.2023.09.019 · Cited by 13

A smartphone-based survey in mHealth to investigate the introduction of the artificial intelligence into cardiology

Giansanti, D., & Monoscalco, L. (2021) · mHealth · doi:10.21037/mhealth-19-188 · Cited by 13

Severe Analysis of Cardiac Disease Detection using the Wearable Device by Artificial Intelligence

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