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

Deep learning for biomedical applications

Freitas, J. D., Glicksberg, B. S., Johnson, K. W., & Miotto, R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820273-9.00004-x · Cited by 4

A Weight Based Labeled Classifier Using Machine Learning Technique for Classification of Medical Data

Ahmed, M. Z., & Mahesh, C. (2021) · Revue d intelligence artificielle · doi:10.18280/ria.350104 · Cited by 4

Künstliche Intelligenz in der pränatalen kardialen Diagnostik

Weichert, J., Welp, A., Scharf, J. L., Dracopoulos, C., Rody, A., & Gembicki, M. (2021) · Der Gynäkologe · doi:10.1007/s00129-021-04890-6 · Cited by 4

Chronic Disease Prediction Using Machine Learning

Meghana, M., Shashank, S., & Mathew, T. (2021) · IJARCCE · doi:10.17148/ijarcce.2021.10663 · Cited by 4

Chronic Disease Prediction Using Machine Learning

Korke, V. K., Chowdhary, V., Keri, S. M., Patil, P. S., & Nandyal, S. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.46166 · Cited by 4

Tailored Risk Stratification in Severe Mitral Regurgitation and Heart Failure Using Supervised Learning Techniques

Heitzinger, G., Spinka, G., Prausmüller, S., Pávó, N., Dannenberg, V., Donà, C., Koschutnik, M., Kammerlander, A. A., Nitsche, C., Arfsten, H., et al. (2022) · JACC Advances · doi:10.1016/j.jacadv.2022.100063 · Cited by 4

Identifying Predictors of Psychological Problems Among Adolescents With Congenital Heart Disease for Referral to Psychological Care: A Pilot Study

Gosnell, J. M., Finn, M. T. M., Marckini, D. N., Molla, A., & Sowinski, H. A. (2022) · CJC Pediatric and Congenital Heart Disease · doi:10.1016/j.cjcpc.2022.12.001 · Cited by 4

Machine Learning for Ischaemic Heart Disease Diagnostic Analysis

Xing, R., & Meng, J. (2022) · IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) · doi:10.1109/ecbios54627.2022.9944997 · Cited by 4

Role of Deep Learning in Computed Tomography

Garg, Y., Seetharam, K., Sharma, M., Rohita, D., & Nabi, W. (2023) · Cureus · doi:10.7759/cureus.39160 · Cited by 4

Will Artificial Intelligence Provide Answers to Current Gaps and Needs in Chronic Heart Failure?

Boccuto, F., Rosa, S. D., Torella, D., Veltri, P., & Guzzi, P. H. (2023) · Applied Sciences · doi:10.3390/app13137663 · Cited by 5

Improving Cardiovascular Disease Prognosis Using Outlier Detection and Hyperparameter Optimization of Machine Learning Models

Patil, S. N., & Bhosale, S. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370429 · Cited by 4

Enhancing Cardiovascular Health: A Machine Learning Approach to Predicting Heart Disease

Tripathi, P., Vishwakarma, K., Sahu, S., Vishwakarma, A., & Kori, D. (2023) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic57670.2023.10263914 · Cited by 4

PAPEL DA INTELIGÊNCIA ARTIFICIAL NA PREDIÇÃO DE EVENTOS CARDÍACOS

Lima, M. A. N., Ferreira, A. F., de Lima, M. E. C. A. G., Retto, Y. C. S., Nogueira, A. C. A., Bernardino, S. B., Aurélio, S. M., Sá, C. A., Motta, M. C., Norões, S. D. V., et al. (2024) · Brazilian Journal of Implantology and Health Sciences · doi:10.36557/2674-8169.2024v6n2p2213-2229 · Cited by 4

Determining the recurrence rate of premature ventricular complexes and idiopathic ventricular tachycardia after radiofrequency catheter ablation with the help of designing a machine-learning model

Nasab, E. M., Sadeghian, S., Vasheghani‐Farahani, A., Sharif, A. Y., Kabir, M. F., Karvane, H. B., Zahedi, A., & Bozorgi, A. (2024) · Regenerative Therapy · doi:10.1016/j.reth.2024.03.001 · Cited by 4

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

Kolaszyńska, O., & Lorkowski, J. (2024) · Applied Bionics and Biomechanics · doi:10.1155/2024/2991243 · Cited by 4

Essentials for AI Research in Cardiology: Challenges and Mitigations

Jaltotage, B., & Dwivedi, G. (2024) · CJC Open · doi:10.1016/j.cjco.2024.07.015 · Cited by 5

Forecasting the development of acute kidney injury using a recurrent neural network

Kallenberger, S. M., & Schmidt, C. (2019) · Cardiovascular Research · doi:10.1093/cvr/cvz279 · Cited by 4

Artificial intelligence in cardiovascular medicine: Applications in the diagnosis of infarction and prognosis of heart failure

Díaz, J. J. S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00013-6 · Cited by 4

Cancer diagnostics and treatment decisions using artificial intelligence

Mirnezami, R. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818438-7.00005-8 · Cited by 4

Clinician Cognition and Artificial Intelligence in Medicine

Chang, A. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-823337-5.00007-x · Cited by 4

Construction engineering cost estimation based on artificial intelligence technology

Wu, W., & Huang, H. F. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-219107 · Cited by 4

Key Technologies of Media Data In-Depth Analysis System Based on Artificial Intelligence-Based Big Data

Zheng, Y. (2021) · Mobile Information Systems · doi:10.1155/2021/7191567 · Cited by 4

Artificial Intelligence as an Emerging Tool for Cardiologists

Ledziński, Ł., & Grześk, G. (2023) · International Electronic Conference on Biomedicines (IECBM) · doi:10.3390/ecb2023-14339 · Cited by 4

Artificial Intelligence Based Commercial Non‐Invasive and Invasive Devices for Heart Failure Diagnosis and Prediction

Kulkarni, K., Isselbacher, E. M., & Armoundas, A. A. (2022) · Wiley eBooks · doi:10.1002/9781119813040.ch12 · Cited by 4

Invasive, Non‐Invasive, Machine Learning, and Artificial Intelligence Based Methods for Prediction of Heart Failure

Takçı, H. (2022) · Wiley eBooks · doi:10.1002/9781119813040.ch1 · Cited by 4

Advancing Our Understanding of Women's Cardiovascular Health Through Digital Health and Artificial Intelligence

Morales-Lara, A. C., Garzon-Siatoya, W. T., Campos, B. A. F., & Adedinsewo, D. A. (2023) · JACC Advances · doi:10.1016/j.jacadv.2023.100272 · Cited by 4

Modeling Clinical Trial Attrition Using Machine Intelligence: A driver analytics case study using 1,325 trials representing one million patientsPreprint

Hutchison, E. R., Zhang, Y., Nampally, S., Neelufer, I. K. A., Malkov, V., Weatherall, J., Khan, F., & Shameer, K. (2021) · medRxiv · doi:10.1101/2021.11.12.21266277 · Cited by 4

AI-Driven Rehabilitation: Personalizing Cardiac Care Post-Diagnosis

Kosaraju, D. (2024) · Galore International Journal of Health Sciences and Research · doi:10.52403/gijhsr.20240121 · Cited by 3

Explainable machine learning model for predicting decline in platelet count after interventional closure in children with patent ductus arteriosus

Zhang, S., Zhang, Y.-D., Li, H., Wang, Q., Ye, Q., Wang, X. C., Xia, T., He, Y., Rong, X., Wu, T., et al. (2025) · Frontiers in Pediatrics · doi:10.3389/fped.2025.1519002 · Cited by 3

Diabetes Monitoring and Prediction Using Computational Intelligence Techniques: A Systematic Review

Nayak, P., Jyothi, J., Harika, V., Swaraja, K., & Hanuman, A. S. (2025) · SN Computer Science · doi:10.1007/s42979-025-03802-y · Cited by 3

Artificial Intelligence in Hypertrophic Cardiomyopathy: Advances, Challenges, and Future Directions for Personalized Risk Prediction and Management

Mohyeldin, M., Mohamed, F. O., Molina, M., Towfig, M. F., Mustafa, A., Elhussein, A. H., Alamin, F., Khaja, M., & Jadhav, P. (2025) · Cureus · doi:10.7759/cureus.87907 · Cited by 3

Unveiling facilitators and barriers to artificial intelligence implementation in cardiac healthcare: Rationale and design of the INSIGHT-AI France study, from the Artificial Intelligence Working Group and the National College of Cardiologists in Training of the French Society of Cardiology

Trimaille, A., Lafitte, S., Bègue, F., Beuque, G., Weizman, O., Lucain, P., Bouali, N., Garban, T., Villacèque, M., Barraud, J., et al. (2025) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2025.06.078 · Cited by 3

AI-Automated Risk Operative Stratification for Severe Aortic Stenosis: A Proof-of-Concept Study

Garín, D., Arroyo, D., Skalidis, I., di Cicco, P., Ferry, C., Bennar, W., Puricel, S., Meier, P., Togni, M., & Cook, S. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14238304 · Cited by 3

Artificial intelligence–based prediction of cardiothoracic intensive care unit length of stay: A comparative machine learning approach

Pereira, R. M., Luís, F., Abecasis, M., Nobre, Â. L., Moita, L. F., & Velho, T. R. (2025) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2025.11.020 · Cited by 3

Stay Human or Go Machine? The Fate of Human Judgement in AI

Toscani, G. (2025) · Global Philosophy · doi:10.1007/s10516-025-09769-y · Cited by 3

Comparative bibliometric analysis of artificial intelligence-assisted polyp diagnosis and AI-assisted digestive endoscopy: trends and growth in AI gastroenterology (2003–2023)

Peng, Z., Wang, X., Li, J., Sun, J., Wang, Y., Li, Y., Li, W., Zhang, S., Wang, X., & Pei, Z. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1438979 · Cited by 3

The Role of Artificial Intelligence in Predicting and Managing Pediatric Fracture Overgrowth: A Comprehensive Review

Marletta, D. A., Nanni, M., Giuca, G., Sanzarello, I., Zampogna, B., & Leonetti, D. (2024) · Applied Sciences · doi:10.3390/app142411652 · Cited by 3

Cardiovascular Disease Analysis and Diagnosis Using Machine Learning Models

Barthwal, A., Ranjan, S., & Avikal, S. (2024) · International Conference on Advancements and Key Challenges in Green Energy and Computing (AKGEC) · doi:10.1109/akgec62572.2024.10868066 · Cited by 3

Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic cardiomyopathy

Chen, J., Xiao, D., Liu, Y.-J., Wang, Z., Chen, Z.-H., Li, R., Li, L., He, R., Jiang, S., Chen, X., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-97534-x · Cited by 3

The Complexity of eHealth Architecture: Lessons Learned from Application Use Cases

Barsotti, A., Gerl, A., Wilhelm, S., Donati, M., Dalmiani, S., & Passino, C. (2025) · Computers · doi:10.3390/computers14090371 · Cited by 3

Artificial Intelligence in Electrocardiography: From Automated Arrhythmia Detection to Predicting Hidden Cardiovascular Disease

Elantary, R., & Othman, S. (2025) · Cureus · doi:10.7759/cureus.94065 · Cited by 3

Monitoring Intrapartum Fetal Distress by Cardiotocography & Observe its Relation with Umbilical Cord Blood Sampling & Perinatal Outcome

Sharmin, Z., Runa, P. K. N., Chawdhury, S. J., Alam, S. T., Tabassum, M., Chowdhury, L., & Marjansultana, M. D. (2022) · Scholars International Journal of Obstetrics and Gynecology · doi:10.36348/sijog.2022.v05i06.003 · Cited by 3

Application of Northern Goshawk Back-Propagation Artificial Neural Network in the Prediction of Monohydroxycarbazepine Concentration in Patients with Epilepsy

Xu, Y., Shao, R., Yang, M., Chen, M., Xu, J., & Dai, H. (2024) · Advances in Therapy · doi:10.1007/s12325-024-02792-2 · Cited by 3

A Machine Learning Approach for Risk Prediction of Cardiovascular Disease

Panda, S., Palei, S., Samartha, M. V. S., Jena, B., & Saxena, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-58174-8_27 · Cited by 3

New Innovations to Address Sudden Cardiac Arrest

Shen, C. P., Bhavnani, S. P., & Rogers, J. (2024) · US Cardiology Review · doi:10.15420/usc.2023.25 · Cited by 3

Unlocking Cancer Prevention In The Era Of Ai: Machine Learning Models For Risk Stratification And Personalized Intervention

Khandakar, S. (2024) · doi:10.53555/kuey.v30i8.7248 · Cited by 3

Challenges, Limitations, and Ethical Considerations of AI in Immunology and Healthcare

Rajchenberg, D., Hess, R., Lins, M. P., Manor, A., Degany, O., & Shoenfeld, Y. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9725-1.ch014 · Cited by 3

Machine learning for predictive analytics

Kashyap, S., Corey, K., Kansal, A., & Sendak, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820273-9.00003-8 · Cited by 3

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

Uso da inteligência artificial para predição de acidentes de trabalho com materiais biológicos em profissionais da saúde

Groto, A. D., Perlin, C. M., de Andrade, S. M., & Salamanca, M. A. B. (2021) · Research Society and Development · doi:10.33448/rsd-v10i12.19743 · Cited by 3

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram

Ding, Z., Wang, G., Yang, H., Zhang, P., Fu, D., Yang, Z., Wang, X., Wang, X., Xia, Z., Zhang, C., et al. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02420-z · Cited by 3

An analytical study for Pneumonia Detection towards building an intelligent system using Image Data Generator

Arora, P., Kapse, V. M., Sinha, S., & Gera, S. (2021) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito51393.2021.9596434 · Cited by 3

Prototyping neural networks to evaluate the risk of adverse cardiovascular outcomes in the population

Bogdanov, L. A., Komossky, E. A., Voronkova, V. V., Tolstosheev, D. E., Martsenyuk, G. V., Сергеевна, А. А., Владимировна, И. Е., Kutikhin, A. G., & Tsygankova, D. P. (2021) · Fundamental and Clinical Medicine · doi:10.23946/2500-0764-2021-6-4-67-81 · Cited by 3

Artificial intelligence for predicting various conditions in spine surgery: a systematic review

Переверзев, В. С., Kazmin, A. I., Сажнев, М. Л., Андреевич, П. А., & Колесов, С. В. (2021) · Genij Ortopedii · doi:10.18019/1028-4427-2021-27-6-813-820 · Cited by 3

Study on the Spatial Structure and City Form Construction of River Valley‐Type Cities in the Context of Artificial Intelligence—A Case Study of Northwest China

Liu, X., Luo, Y., & Huang, M. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/4202745 · Cited by 3

Künstliche Intelligenz und Gesundheit. Ethische, philosophische und sozialwissenschaftliche Explorationen

Sonar, A., & Weber, K. (2022) · Franz Steiner Verlag eBooks · doi:10.25162/9783515129770 · Cited by 5

Prediction of Coronary Artery Disease using Artificial Intelligence – A Systematic Literature Review

Ramanathan, G., & Jagadeesha, S. N. (2023) · International Journal of Health Sciences and Pharmacy · doi:10.47992/ijhsp.2581.6411.0097 · Cited by 3

Assessment of Awareness, Perception, and Opinions Towards Artificial Intelligence among Health Care Professionals and Medical Students at Tertiary Care Teaching Hospital: A Cross-sectional Study

Rahul, S., -, M. N., Mudgal, S. M., & Doddayya, H. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i01.11678 · Cited by 3

Enhancing Cardiovascular Disease Prediction via Hybrid Deep Learning Architectures: A Step Towards Smart Healthcare

Murtaza, A. A., Saher, A., Mohyuddin, H., Moosavi, S. K. R., Zafar, M. H., & Sanfilippo, F. (2023) · International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE) · doi:10.1109/etecte59617.2023.10396716 · Cited by 3

Artificial intelligence in cardiology: Present state and prospective directions

Alharbi, Y. (2024) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2024.101012 · Cited by 3

Recent trends and digital technology applications in lower limb injury rehabilitation

Sali, S., Chai, R., & Ganesan, B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22270-2.00007-1 · Cited by 3

Artificial intelligence versus human analysis: Interpreting data in elderly fat reduction study

Sporek, P., & Konieczny, M. (2025) · Advances in Integrative Medicine · doi:10.1016/j.aimed.2024.12.011 · Cited by 3

Artificial Intelligence implemented to recognize patterns of sustainable areas by evaluating the database of socioenvironmental safety restrictions

Neto, J. L. A., Gonçalves, A. L. S., da Cruz, B. C. C., Gomes, L. L., & Costa, D. C. L. (2021) · Research Society and Development · doi:10.33448/rsd-v10i10.18841 · Cited by 3

Use of Smartphones and Wearables for Arrhythmia Monitoring

Sanders, D. J., Wasserlauf, J., & PASSMAN, R. S. (2021) · Cardiac Electrophysiology Clinics · doi:10.1016/j.ccep.2021.04.004 · Cited by 3

Precision Medicine for Aortic Stenosis

Chandrashekhar, Y., & Johnson, K. W. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.12.005 · Cited by 3

Digital Phenotyping of Myocardial Dysfunction With 12-Lead ECG

Nasir, K., & Khera, R. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.07.001 · Cited by 3

The “Shocking” Reality of ICD Therapies

Deo, R., & Pothineni, N. V. K. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.04.019 · Cited by 3

Digital Transformation in Medicine to Enhance Quality of Life, Longevity, and Health Equity

Itchhaporia, D., Baliga, R. R., & Bossone, E. (2022) · Heart Failure Clinics · doi:10.1016/j.hfc.2022.02.013 · Cited by 3

Artificial Intelligence in Cardiovascular Medicine: A Comprehensive Clinical ReviewPreprint

Ganipineni, V. D. P., Gutlapalli, S. D., Paramsothy, J., Okorie, I. J., Ugwendum, D. F., Farid, M., Fabián, D., Kailayanathan, T., Piccione, G., Idavalapati, A. S. K. K., et al. (2023) · Preprints.org · doi:10.20944/preprints202305.1061.v1 · Cited by 3

Editorial: The role of artificial intelligence technologies in revolutionizing and aiding cardiovascular medicine

Attallah, O., Ma, X., & Sedky, M. (2025) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2025.1588983 · Cited by 3

Towards Machine Learning Techniques for Enhanced Prediction of Coronary Artery Disease

Kukreti, A., & Maji, S. (2024) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc460750.2024.10649400 · Cited by 2

Artificial intelligence systems for predicting chronic ischemic heart disease outcomes in cardiac surgery patients based on presence of anemia: a literature review

Kalyuta, T. Y., Emelyanova, I. P., Suvorov, V. V., & Fedonnikov, A. (2024) · Russian Medicine · doi:10.17816/medjrf635256 · Cited by 2

La tecnología y su impacto en la educación musical en niños

Salazar, M. A. M., & Baluis, W. L. R. (2024) · Horizontes Revista de Investigación en Ciencias de la Educación · doi:10.33996/revistahorizontes.v8i35.885 · Cited by 2

Accuracy of Artificial Intelligence Based Chatbots in Analyzing Orthopedic Pathologies: An Experimental Multi-Observer Analysis

Gehlen, T., Joost, T., Solbrig, P., Stahnke, K., Zahn, R., Jahn, M., Amini, D. A., & Back, D. A. (2025) · Diagnostics · doi:10.3390/diagnostics15020221 · Cited by 2

A Comprehensive Analysis & Performance Evaluation of Machine Learning Techniques in Heart Disease Diagnosis

Sharma, J., Thakur, P., Singh, K., & Gupta, N. (2024) · International Conference on Sustainable Communication Networks and Application (ICSCNA) · doi:10.1109/icscna63714.2024.10863961 · Cited by 2

Harnessing artificial intelligence for theragnostic applications: Current landscape and future directions

Pande, A., Kumar, A., & Anjankar, A. P. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2025218 · Cited by 3

Artificial Intelligence in Echocardiography: Where Do We Stand?

Das, D., & Sahoo, S. (2025) · Journal of Cardiac Critical Care TSS · doi:10.25259/jccc_59_2024 · Cited by 2

Selection of features for modeling the risk of fatal outcomes in patients after myocardial infarction or unstable angina

Швец, Д. А., & Povetkin, S. V. (2025) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2025-4102 · Cited by 2

The Application of Machine Learning in Warfarin Dose Precision for Diabetic Patients Treated with Statins: A Comparative Study

Ghajar, M., Ghafourian, M. S., Tarkiani, S., Tork, A. N., Ramezani, A., Zolfaghari, B., & Palangi, M. G. (2025) · Cardiovascular Drugs and Therapy · doi:10.1007/s10557-025-07690-5 · Cited by 2

Artificial intelligence: a promising tool for the clinical cardiologist

Escobar, C., Fácila, L., Vidal-Pérez, R., Lapeña, A. P., Vivas, D., Martín, A. G., Manzano‐Fernández, S., Caballero, E. G., Barrios, V., & Freixa‐Pamias, R. (2025) · Expert Review of Cardiovascular Therapy · doi:10.1080/14779072.2025.2520830 · Cited by 2

Neurosurgery 4.0: A Scoping Review of Current Trends and Challenges in Specialized Smart Hospitals

Shabo, E., Sarikaya-Seiwert, S., Borger, V., Schneider, M. B., Vatter, H., & Hager, L. (2025) · Cureus · doi:10.7759/cureus.92673 · Cited by 2

Cardiovascular Therapeutics at the Crossroads: Pharmacological, Genetic, and Digital Frontiers

Vetrano, E., Caturano, A., Nilo, D., Lorenzo, G. D., Tagliaferri, G., Piacevole, A., Donnarumma, M., Iadicicco, I., Picco, S., Moretto, S. M., et al. (2025) · Pharmaceuticals · doi:10.3390/ph18111703 · Cited by 2

Characterizing industry payments for FDA-approved AI medical devices

Bergman, A., Patel, T. A., & Shah, K. P. (2025) · Health Affairs Scholar · doi:10.1093/haschl/qxaf211 · Cited by 2

Protective predictors of cardiovascular disease: an explainable AI approach

Minh, L. H. N., Kha, H. Q., Huynh, P. K., Huynh, P. K., Nguyen, P. K., Nguyen, D., Trang, L. D. T., Tran, V. N., Bui, Q. V. P., Pham, H. T., et al. (2025) · Public Health · doi:10.1016/j.puhe.2025.106050 · Cited by 2

Nebivolol in the therapeutic landscape of heart failure: Mechanisms and clinical outcomes

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