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

Predicting ICU mortality in heart failure patients based on blood tests and vital signs

Wang, Y., Rong, J., Wei, Z., Bai, X., & Deng, Y. (2025) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2025.1590367 · Cited by 6

Application of artificial intelligence in echocardiography from 2009 to 2024: a bibliometric analysis

Liu, Y., Zhao, L., Tu, B., Wang, J., He, Y., Jiang, R., Wu, X., Wen, W., & Jian, L. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1587364 · Cited by 6

Personalized Treatment of Patients with Coronary Artery Disease: The Value and Limitations of Predictive Models

Greco, A., & Capodanno, D. (2025) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd12090344 · Cited by 6

Artificial intelligence in medicine: a position paper by the Italian Society of Internal Medicine

Balsano, C., Cabitza, F., Cicco, S., Gori, M., Malerba, D., Montagna, M., Tarquini, R., Vacca, A., & The Working Group on Artificial Intelligence, D. T. O. T. I. S. O. I. M. (. (2025) · Internal and Emergency Medicine · doi:10.1007/s11739-025-04146-4 · Cited by 7

Clinically informed semi-supervised learning improves disease annotation and equity from electronic health records: a glaucoma case study

Moradi, M., Shah, R., Fujita, A., Bineshfar, N., Vu, D. M., Aziz, K., Liebman, D. L., Hashemabad, S. K., Wang, M., Elze, T., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02267-w · Cited by 6

Machine learning models using multimodal data accurately predict chemotherapy-induced cardiotoxicity in breast cancer

Chen, K., An, Y., Wang, Z., & Nie, F. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2025.1707889 · Cited by 6

Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review

Al-Smadi, F., Al-Smadi, S., Xie, X., Abudourusuli, X., Ouyang, L., Zeng, R., Du, L., Liao, Y., Mi, B., & Liu, G. (2026) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2026.1765936 · Cited by 7

Privacy Considerations in the Canadian Regulation of Commercially-Operated Healthcare Artificial Intelligence

Murdoch, B., Jandura, A., & Caulfield, T. (2022) · Canadian Journal of Bioethics · doi:10.7202/1094696ar · Cited by 7

Electrocardiogram based cardiovascular disease detection with Ensemble Learning Classifier

K, S. A., Gururaj, V., Shankar, S. P., Supriya, M. S., & Bharadwaj, A. (2022) · International Conference on Circuits, Control, Communication and Computing (I4C) · doi:10.1109/i4c57141.2022.10057719 · Cited by 6

Design and Implementation of Heart Disease Prediction Using Artificial Neural Network

Chavan, M., Singh, S. K., Bansod, S., & Pal, P. (2023) · International Conference on Science Technology Engineering and Mathematics (ICONSTEM) · doi:10.1109/iconstem56934.2023.10142267 · Cited by 6

AI-Based Optimal Treatment Strategy Selection for Female Infertility for First and Subsequent IVF-ET Cycles

Wang, R., Pan, W., Yu, L., Zhang, X., Pan, W., Hu, C., Wen, L., Jin, L., & Liao, S. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01967-8 · Cited by 6

Multi-center atrial fibrillation electrocardiogram (ECG) classification using Fourier space convolutional neural networks (FD-CNN) and transfer learning

Vasconcelos, L., Martínez, B. O. P., Kent, M., Ansari, S., Ghanbari, H., & Nenadic, I. (2023) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2023.09.010 · Cited by 6

The New Era: Transforming Healthcare Quality with Artificial Intelligence

İncegil, D., Kayral, İ. H., & Şenel, F. Ç. (2023) · Accounting, finance, sustainability, governance & fraud · doi:10.1007/978-981-99-6327-0_13 · Cited by 6

Pulse wave-based evaluation of the blood-supply capability of patients with heart failure via machine learning

Wang, S., Ono, R., Wu, D., Aoki, K., Kato, H., Iwahana, T., Okada, S., Kobayashi, Y., & Liu, H. (2024) · BioMedical Engineering OnLine · doi:10.1186/s12938-024-01201-7 · Cited by 6

A comprehensive review on heart disease prognostication using different artificial intelligence algorithms

Fathima, A. J., & Fasla, M. M. N. (2024) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2024.2319706 · Cited by 6

The Role of Artificial Intelligence in the Diagnosis of Neoplastic Diseases: A Systematic and Bibliometric Review

Villavicencio, H. M. E., Gamboa-Cruzado, J., López-Goycochea, J., & Soto, L. S. (2024) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v20i04.45429 · Cited by 6

Global research trends of Artificial Intelligence and Machine Learning applied in medicine: A bibliometric analysis (2012–2022)

Nicolò, V. D., & Torre, D. L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00004-1 · Cited by 6

Artificial intelligence in cardiology: a bibliometric study

Zhang, Y. (2024) · American Journal of Translational Research · doi:10.62347/hsfe6936 · Cited by 6

Optimizing Acute Coronary Syndrome Patient Treatment: Leveraging Gated Transformer Models for Precise Risk Prediction and Management

Mei, Y., Jin, Z., Ma, W., Yingjun, M., Deng, N., Fan, Z., & Wei, S. (2024) · Bioengineering · doi:10.3390/bioengineering11060551 · Cited by 6

A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects

Akkutay-Yoldar, Z., Yoldar, M. T., Akkaş, Y. B., Şurak, S., Garip, F., Turan, O., Ekizoğlu, B., Yüca, O. C., Özkul, A., & Ünver, B. (2025) · Scientific Reports · doi:10.1038/s41598-025-89639-0 · Cited by 6

Can Artificial Intelligence Software be Utilised for Thyroid Multi‐Disciplinary Team Outcomes?

Habeeb, A., Lim, K. H., Kochilas, X., Bhat, N., Amen, F., & Chan, S. (2025) · Clinical Otolaryngology · doi:10.1111/coa.14305 · Cited by 6

Federated and ensemble learning framework with optimized feature selection for heart disease detection

Hrizi, O., Gasmi, K., Alyami, A., Alkhalil, A., Alrashdi, I., Alqazzaz, A., Ammar, L. B., Mrabet, M., Abdalrahman, A. E. M., & Yahyaoui, S. (2025) · AIMS Mathematics · doi:10.3934/math.2025334 · Cited by 6

Enhanced prediction of atrial fibrillation and mortality among patients with congenital heart disease using nationwide register-based medical hospital data and neural networks

Giang, K. W., Helgadóttir, S., Dellborg, M., Volpe, G., & Mandalenakis, Z. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab065 · Cited by 6

Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks

Zafar, H., Zafar, J., & Sharif, F. (2022) · Optics · doi:10.3390/opt3010002 · Cited by 6

Continuous heart rate dynamics preceding in-hospital pulseless electrical activity or asystolic cardiac arrest of respiratory etiology

Shan, R., Yang, J. J., Kuo, A., Lee, R. J., Hu, X., Boyle, N. G., & Do, D. H. (2022) · Resuscitation · doi:10.1016/j.resuscitation.2022.07.026 · Cited by 6

A New Era in Cardiometabolic Management: Unlocking the Potential of Artificial Intelligence for Improved Patient Outcomes

Nashwan, A. J. (2023) · Endocrine Practice · doi:10.1016/j.eprac.2023.06.003 · Cited by 7

Digital Health Secondary Prevention Using Co-Design Procedures: Focus Group Study With Health Care Providers and Patients With Myocardial Infarction

Pelly, M. L., Fatehi, F., Liew, D., & Verdejo‐García, A. (2023) · JMIR Cardio · doi:10.2196/49892 · Cited by 6

A Machine Learning Based Analysis of Heart Disease Diagnosis

Hamid, A., & Ali, K. B. (2023) · International Conference on Integration of Computational Intelligent System (ICICIS) · doi:10.1109/icicis56802.2023.10430309 · Cited by 6

Ethical Implications of AI in Healthcare

Dhawan, S., & Kumar, K. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem29006 · Cited by 6

A comprehensive review on deep cardiovascular disease detection approaches: its datasets, image modalities and methods

Saha, P., De, A., Roy, S. D., & Bhowmik, M. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18953-y · Cited by 6

Predicting COPD Severity Using Machine Learning and GOLD Criteria

Kinikar, A., Chandwani, M., & Rane, T. A. (2024) · International Conference for Innovation in Technology (INOCON) · doi:10.1109/inocon60754.2024.10511329 · Cited by 6

Two-step learning for crowdsourcing data classification

Yu, H., Li, J., Wu, Z., Xu, H. L., & Zhu, L. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12793-4 · Cited by 6

2022 Artificial intelligence primer for the nuclear cardiologist

Motwani, M. (2022) · Journal of Nuclear Cardiology · doi:10.1007/s12350-022-03049-7 · Cited by 6

Reducing the Heart Failure Burden in Romania by Predicting Congestive Heart Failure Using Artificial Intelligence: Proof of Concept

Pană, M.-A., Busnatu, Ș., Șerbănoiu, L. I., Vasilescu, E., Popescu, N., Andrei, C. L., & Sinescu, C. J. (2021) · Vide Leaf, Hyderabad eBooks · doi:10.37247/paasc.1.22.13 · Cited by 6

High-performance association rule mining: Mortality prediction model for cardiovascular patients with COVID-19 patterns

Nadakinamani, R. G., Reyana, A., Gupta, Y., Kautish, S., Ghorashi, S. A., Jamjoom, M. M., & Mohamed, A. W. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2023.03.036 · Cited by 6

Artificial Intelligence Applications to Improve Risk Prediction Tools in Electrophysiology

Kowlgi, G. N., Ezzeddine, F. M., & Kapa, S. (2020) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-020-00649-1 · Cited by 6

Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients

Thomason, N., Monlezun, D. J., Javaid, A. I., Filipescu, A. G., Koutroumpakis, E., Shobayo, F. O., Kim, P. Y., Lopez‐Mattei, J., Çilingiroğlu, M., Iliescu, G., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.793877 · Cited by 6

It Is Time for Us to Get Artificially Intelligent!

Singh, J. P. (2019) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2018.12.003 · Cited by 6

Development of an ensemble machine learning prognostic model to predict 60-day risk of major adverse cardiac events in adults with chest painPreprint

Kennedy, C. J., Mark, D. G., Huang, J., van der Laan, M. J., Hubbard, A., & Reed, M. E. (2021) · medRxiv · doi:10.1101/2021.03.08.21252615 · Cited by 6

Robotics and Artificial Intelligence and Their Impact on the Diagnosis and Treatment of Cardiovascular Diseases

Saeed, A., AlShafea, A., Saeed, A. B., Nasser, M., & Ali, R. (2023) · Cureus · doi:10.7759/cureus.42252 · Cited by 6

Navigating AI in Cardiology: A Scoping Review of Integration through Clinical Decision Support Systems for Acute Coronary Syndrome

Chen, S.-H., Wu, C.-C., & Chen, K.-F. (2025) · Biomedical Journal · doi:10.1016/j.bj.2025.100853 · Cited by 5

Harnessing artificial intelligence (AI) techniques for the diagnosis of microbial disease in humans

Ogidi, O. I., & Tobia, P.-E. S. (2024) · Methods in microbiology · doi:10.1016/bs.mim.2024.05.004 · Cited by 6

Artificial intelligence applications in free flap microvascular reconstruction: preoperative planning, intraoperative assessment, and postoperative monitoring

Villavisanis, D. F., Elhage, S. A., Crystal, D. T., Terry, P. H., Serletti, J. M., & Percec, I. (2025) · Artificial Intelligence Surgery · doi:10.20517/ais.2024.89 · Cited by 5

Artificial intelligence in cardiovascular medicine: An exoskeleton for perception, reasoning and action

Desroche, L., Trimaille, A., Garban, T., & Bouleti, C. (2025) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2025.07.012 · Cited by 5

Application of artificial intelligence in predicting the results of open-heart surgery: a scoping review

Khodaveisi, T., Aslani, N., Amiri, P., Kamrani, F., & Saeedi, S. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03243-w · Cited by 5

Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention

Ledziński, Ł., Grześk, G., Ziołkowski, M., Waligóra, M., Kurzyna, M., Mularek‐Kubzdela, T., Smukowska-Gorynia, A., Skoczylas, I., Chrzanowski, Ł., Błaszczak, P., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14238325 · Cited by 5

Multi-perspective machine learning MPML: A high-performance and interpretable ensemble method for heart disease prediction

Miller, S. T., Logan, K. A., Anderson, R., Cowell, P. E., Busby-Earle, C., & Dionne-Morris, L. (2026) · Machine Learning with Applications · doi:10.1016/j.mlwa.2026.100836 · Cited by 5

Multiple machine-learning-driven metabolic frameworks for long-term prognostic risk assessment in patients with coexisting hypertension and obstructive sleep apnea:insights from a multicenter cohort study

Xu, Q., Xu, Y., Yijun, W. A. N. G., Liu, S., Yang, Y., Zhao, H., Duan, S., & Wang, J. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1739374 · Cited by 5

Risk Prediction in Cardio-Oncology: Conceptual and Methodological Considerations

Sen, J., Amir, E., Austin, P. C., Marwick, T. H., McIntosh, C., Thavendiranathan, P., & Abdel-Qadir, H. M. (2026) · JACC CardioOncology · doi:10.1016/j.jaccao.2026.01.006 · Cited by 5

The role of artificial intelligence in predicting cardiovascular outcomes: a systematic review and meta-analysis

Riipa, M. B., Ahmed, F., Rony, M. K. K., Hossain, A., Islam, A., Utsho, M. R., Kamal, M. B., Sharmin, S., & Tasnim, A. F. (2026) · Biostatistics & Epidemiology · doi:10.1080/24709360.2026.2670804 · Cited by 8

AI-Aided Disease Prediction in Visualized Medicine

Du, J., Huang, M., & Liu, L. (2023) · Advances in experimental medicine and biology · doi:10.1007/978-981-32-9902-3_6 · Cited by 5

The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis: Current Insights and Future Directions

Cerdas, M. G., Pandeti, S., Reddy, L., Grewal, I., Rawoot, A., Anis, S., Todras, J., Chouihna, S., Salma, S., Lysak, Y., et al. (2024) · Cureus · doi:10.7759/cureus.72311 · Cited by 5

A Scoping Review of the Use of Artificial Intelligence in the Identification and Diagnosis of Atrial Fibrillation

Menezes, A. D. S., Silva, A. L. F. E., Silva, L. R., de Lima, K. B. A., & de Oliveira, H. L. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14111069 · Cited by 5

ChatGPT-Assisted Machine Learning for Chronic Disease Classification and Prediction: A Developmental and Validation Study

Abbas, S., Iftikhar, M., Shah, M. M., & Khan, S. J. (2024) · Cureus · doi:10.7759/cureus.75851 · Cited by 5

Comprehensive SHAP Values and Single-Cell Sequencing Technology Reveal Key Cell Clusters in Bovine Skeletal Muscle

Guo, Y., Ma, F., Li, P., Guo, L., Liu, Z., Huo, C., Shi, C., Zhu, L., Gu, M., Na, R., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26052054 · Cited by 5

Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment

Du, S., Wu, Y., Tao, J., Shu, L., Yan, T., Xiao, B., Lv, S., Ye, M., Gong, Y., Zhu, X., et al. (2025) · Therapeutics and Clinical Risk Management · doi:10.2147/tcrm.s504745 · Cited by 5

Explainable Artificial Intelligence Approach to Heart Attack Risk Prediction

TURAN, T. (2025) · Karadeniz Fen Bilimleri Dergisi · doi:10.31466/kfbd.1473382 · Cited by 5

Automated generation of echocardiography reports using artificial intelligence: a novel approach to streamlining cardiovascular diagnostics

Syryca, F., Gräßer, C., Trenkwalder, T., & Nicol, P. (2025) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-025-03382-1 · Cited by 7

Artificial intelligence-driven techniques for interventional cardiology: A current appraisal

Dash, D., Dash, U., & Abu-Dakka, B. (2025) · Cardiovascular revascularization medicine · doi:10.1016/j.carrev.2025.06.016 · Cited by 5

Construction and application of machine learning models for predicting intradialytic hypotension

Wang, P., Xu, N., Wu, L., Hong, Y., Qu, Y., Ren, Z., Luo, Q., & Cai, K. (2025) · PLoS ONE · doi:10.1371/journal.pone.0333357 · Cited by 5

Machine learning methods as a tool for diagnostic and prognostic research in cardiovascular disease

Khidirova, C., Sadikova, S., Mukhsinov, S., Nashvandova, G., & Mirzaeva, S. (2021) · International Conference on Information Science and Communications Technologies (ICISCT) · doi:10.1109/icisct52966.2021.9670168 · Cited by 5

Application in medicine: Has artificial intelligence stood the test of time

Sajid, M. I., Ahmed, S., Waqar, U., Tariq, J., Chundrigarh, M., Balouch, S. S., & Abaidullah, S. (2022) · Chinese Medical Journal · doi:10.1097/cm9.00000000000020s8 · Cited by 5

Cardiac scoring systems, coronary artery disease and major adverse cardiovascular events: A scoping review

Premsagar, P., Aldous, C. M., & Esterhuizen, T. M. (2023) · South African Family Practice · doi:10.4102/safp.v65i1.5683 · Cited by 5

Árboles de clasificación obtenidos mediante inteligencia artificial para la predicción de insuficiencia cardiaca tras el síndrome coronario agudo

Cordero, A., Bertomeu‐González, V., Segura, J., Morales, J., Álvarez, B. Á., Escribano, D., Rodríguez‐Mañero, M., Cid-Álvarez, B. A., García‐Acuña, J. M., González‐Juanatey, J. R., et al. (2024) · Medicina Clínica · doi:10.1016/j.medcli.2024.01.040 · Cited by 5

Electrical anisotropy in two-dimensional reduced graphene oxide/ polypyrrole-based ordered conjugated system ensure multi-stimulus response signal adapter

Chai, Y., Chen, R., Ma, X., He, Y., Zhang, H., Xia, T., Chen, J., Wang, Y., Ma, H., Wang, X., et al. (2024) · Science China Materials · doi:10.1007/s40843-024-3099-0 · Cited by 5

Machine Learning and Antibiotic Management

Maviglia, R., Michi, T., Passaro, D., Raggi, V., Bocci, M. G., Piervincenzi, E., Mercurio, G., Lucente, M., & Murri, R. (2022) · Antibiotics · doi:10.3390/antibiotics11030304 · Cited by 5

Cardiovascular Magnetic Resonance Parametric Mapping Techniques for the Assessment of Chronic Coronary Syndromes

Bazmpani, M.-A., Nikolaidou, C., Papanastasiou, C. A., Ziakas, A., & Karamitsos, T. D. (2022) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd9120443 · Cited by 5

Machine learning in digital health, recent trends, and ongoing challenges

Cummins, N., Ren, Z. X., Mallol-Ragolta, A., & Schuller, B. W. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00005-7 · Cited by 6

Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques

Bennasar, M., Banks, D., Price, B. A., & Kardos, A. (2020) · JMIR Cardio · doi:10.2196/16975 · Cited by 5

Artificial intelligence and machine learning in cardiovascular computed tomography

Seetharam, K., Bhat, P., Orris, M., Prabhu, H., Shah, J. M., Asti, D., Chawla, P., & Mir, T. (2021) · World Journal of Cardiology · doi:10.4330/wjc.v13.i10.546 · Cited by 5

Prospect of Artificial Intelligence for the Assessment of Cardiac Function and Treatment of Cardiovascular Disease

Kheradvar, A., Jafarkhani, H., Guy, T. S., & Finn, J. P. (2020) · Future Cardiology · doi:10.2217/fca-2020-0128 · Cited by 5

Artificial intelligence in modern medicine- evolving necessity of the present and role in transforming the future of medical care (Preprint)Preprint

Bhattad, P. B., & Jain, V. K. (2020) · doi:10.2196/preprints.18829 · Cited by 5

Comparing AI-Driven and Heart Team Decision-Making in Multivessel Coronary Artery Disease

Migliaro, S., Celotto, R., Teliti, R., Mariani, S., Altamura, L., & Tomai, F. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14134452 · Cited by 4

Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review

Lee, H. S., Croon, P. M., Lee, M. S., Poterucha, T. J., Lin, C. H., Son, J. M., Jeon, K. H., Tarabanis, C., Lee, S., Kim, K.-H., et al. (2026) · Korean Circulation Journal · doi:10.4070/kcj.2025.0486 · Cited by 4

Diverging regulatory DNA in adaptive medical AI: US agility and EU accountability in lifecycle governance

Lee, J. H., Choi, B., Jeong, K., Suh, S. W., Rhee, H., Kim, J. H., & Son, D.-S. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1758708 · Cited by 4

Data Resource Profile: The Cardiac Analytics and Innovation (CardiacAI) Data Repository

Blake, V., Jorm, L. R., Yu, J. S., Lee, A., Gallego, B., & Ooi, S. (2024) · International Journal of Epidemiology · doi:10.1093/ije/dyae040 · Cited by 4

Acceptance of AI in Health Care for Short- and Long-Term Treatments: Pilot Development Study of an Integrated Theoretical Model

Wichmann, J., Gesk, T. S., & Leyer, M. (2024) · JMIR Formative Research · doi:10.2196/48600 · Cited by 4

Echocardiographic Diagnosis of Hypertrophic Cardiomyopathy by Machine Learning

Farahani, N. Z., Aguirre, M. A., Vizentin, V. K., Enayati, M., Bos, J. M., Pumarejo, A., Larson, K. F., Pasupathy, K. S., Scott, C. G., Zacher, A. L., et al. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.08.009 · Cited by 4

Electrocardiogram-Based Artificial Intelligence for Detection of Low Ejection Fraction: A Contemporary Review

Tran, H. H.-V., Thu, A., Fuertes, A., Twayana, A. R., Mahadevaiah, A., Mehta, K. A., James, M., Basta, M., Weissman, S. I., Frishman, W. H., et al. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000000975 · Cited by 4

The Role of Artificial Intelligence in Heart Failure Diagnostics, Risk Prediction, and Therapeutic Strategies: A Comprehensive Review

Saeed, I., Hashmi, M. U., Khalid, M., Ramzan, H., Ibrahim, M. L., Baig, M. F., Mansoor, S. A., Asad, Z., Abbas, J., & Pillai, S. (2025) · Cureus · doi:10.7759/cureus.87130 · Cited by 4

Exploring the value of ChatGPT in selecting antidiabetic agents for type 2 diabetes

Jang, S. A., Kwon, S., Kim, C. S., Park, S. W., & Kim, K. M. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16630 · Cited by 4

Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology

Metin, İ., & Özdemır, Ö. (2025) · World Journal of Methodology · doi:10.5662/wjm.v15.i4.106854 · Cited by 5

Global research trends in artificial intelligence in cardiovascular medicine: A bibliometric analysis

Su, F., Liu, C., Sun, H., Li, Y., Lv, Z., Wu, X., Wang, Y., Shi, K., Lin, S., Zhao, P., et al. (2025) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2025.10.328 · Cited by 4

Artificial Intelligence and Machine Learning (AI/ML) Revolution in Cardiology Medical Devices: From Diagnosis to Treatment

Pushadapu, V. V. S. K., Srilakshmi, C., Vinitha, B., Babu, P. S., Rahaman, S. A., Chettupalli, A. K., Devadasu, V. R., & Bukke, S. P. N. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-01084-z · Cited by 4

Predictive health index: integrating body composition and heart rate variability metrics with artificial intelligence to predict chronic disease risk and specific chronic non-communicable diseases

Boschiero, D., Gallotta, A., Ferrari, F., Dragoumani, K., Lambrou, G. Ι., Vlachakis, D., & Chrousos, G. P. (2025) · HORMONES · doi:10.1007/s42000-025-00727-2 · Cited by 4

Automated analysis of echocardiographic images: do different artificial intelligence tools provide concordant measurements?

Szasz, T., Cotella, J. I., Slivnick, J. A., Guo, J., Hitschrich, N., Wiebel, H., Schreckenberg, M., Asch, F. M., Mor‐Avi, V., & Lang, R. M. (2026) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-025-03539-y · Cited by 4

Analysis of Machine Learning Models for Heart Disease Prediction and Diagnosis

Bhatta, R. P., & Husain, A. (2026) · NPRC Journal of Multidisciplinary Research. · doi:10.3126/nprcjmr.v3i1.90028 · Cited by 4

Machine Learning for Predicting Coronary Heart Disease Risk in Patients with Hypertension: An Ensemble Modeling Approach

Hassan, F. H., Wang, S., & Miron, A. (2026) · Healthcare Informatics Research · doi:10.4258/hir.2026.32.1.28 · Cited by 4

Artificial Intelligence in Practice – Real-World Examples and Emerging Business Models

Radhakrishnan, J., & Gupta, S. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-64849-7_8 · Cited by 4

Performance Metrics, Algorithms, and Applications of Artificial Intelligence in Vascular and Interventional Neurology

Abdollahifard, S., Farrokhi, A., Mowla, A., & Liebeskind, D. S. (2024) · Neurologic Clinics · doi:10.1016/j.ncl.2024.03.001 · Cited by 4

Assessment of Albumin-Incorporating Scores at Hepatocellular Carcinoma Diagnosis Using Machine Learning Techniques: An Evaluation of Prognostic Relevance

Suárez, M., Martínez-Blanco, P., Gil-Rojas, S., Torres, A., Torralba, M., & Mateo, J. (2024) · Bioengineering · doi:10.3390/bioengineering11080762 · Cited by 4

The Impact of Artificial Intelligence on Internal Medicine Physicians: A Survey of Procedural and Non-procedural Specialties

Mansoor, M. A., Ibrahim, A. F., & Kidd, N. (2024) · Cureus · doi:10.7759/cureus.69121 · Cited by 5

The Future of Artificial Intelligence and Genetic Insights in Precision Cardiovascular Medicine: A Comprehensive Review

Sekar, P. K. C., & Veerabathiran, R. (2024) · Cardiology Discovery · doi:10.1097/cd9.0000000000000136 · Cited by 4

Current advancements of machine learning in healthcare

Deshmukh, S. S., Kulkarni, N., Manoharan, A., Vinarski, B. M., & Ramanujam, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15504-8.00001-6 · Cited by 4

Machine Learning on Prediagnostic Metabolite Data Identifies Etiologic Endotypes of Exfoliation Glaucoma in United States Health Professionals

Hanyuda, A., Zeleznik, O. A., Raita, Y., Negishi, K., Pasquale, L. R., Lasky-Su, J. A., Wiggs, J. L., & Kang, J. H. (2024) · Ophthalmology Science · doi:10.1016/j.xops.2024.100678 · Cited by 4

An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach

Badr, S., Tahri, M., Maanan, M., Kašpar, J., & Yousfi, N. (2025) · Systems Biology in Reproductive Medicine · doi:10.1080/19396368.2024.2445831 · Cited by 4

Re-engineering the clinical approach to suspected cardiac chest pain assessment in the emergency department by expediting research evidence to practice using artificial intelligence. (RAPIDx AI)—a cluster randomized study design

Khan, E., Lambrakis, K., Briffa, T. G., Cullen, L., Karnon, J. D., Papendick, C., Quinn, S. J., Tideman, P., van den Hengel, A., Verjans, J. W. H., et al. (2025) · American Heart Journal · doi:10.1016/j.ahj.2025.02.016 · Cited by 4

Artificial intelligence in cardiovascular practice

Farquhar-Snow, M., Simone, A. E., Singh, S. V., & Bushardt, R. L. (2025) · JAAPA · doi:10.1097/01.jaa.0000000000000204 · Cited by 5

Revolutionizing Cardiovascular Disease Prediction: Stacked Ensemble Models with XGBoost, Neural Networks, and Generative AI Integration

Kumar, N. R. S., Baalaji, K., Sivagnanasekar, G., Vijayarajan, S., R, S. M. A., & C, H. (2025) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai65575.2025.11011879 · Cited by 4

Is proteomics of value in cardiovascular risk assessment?

Arnold, N., & Köenig, W. (2019) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000639 · Cited by 4

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