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This comprehensive review examines the conceptual foundations, clinical applications, and future implications of AI across the CV continuum, spanning prevention, diagnosis, risk stratification, and therapy. Core AI methodologies (including machine learning, deep learning, natural language processing, and computer vision) are discussed in the context of cardiology\u2019s uniquely data-rich environment, encompassing imaging, electrocardiography, electronic health records, wearable devices, and multi-omics data. This systematic review highlights major clinical domains where AI has demonstrated a substantial impact, including CV imaging, ECG interpretation, hypertension and heart failure management, coronary artery disease, acute coronary syndromes, interventional cardiology, and cardiac surgery. AI-driven predictive analytics enable early detection of subclinical disease, improved prognostication, and individualized prevention strategies, while wearable technologies and remote monitoring platforms facilitate continuous, real-world patient surveillance. Emerging applications in pharmacotherapy, drug repurposing, and genomics further reinforce AI\u2019s role in advancing precision cardiology. Equally emphasized are the ethical, legal, and social challenges accompanying AI adoption, such as algorithmic bias, data privacy, cybersecurity, interpretability, and regulatory oversight. Our review underscores the necessity of rigorous clinical validation, transparent model design, and seamless integration into clinical workflows to ensure safety, equity, and physician trust. Ultimately, AI is best positioned as an augmentative tool that complements (but does not replace!) clinical expertise. By fostering hybrid intelligence that integrates human judgment with computational power, AI has the potential to redefine CV care delivery, improve outcomes, and support a more proactive, patient-centered healthcare model.<\/jats:p>","DOI":"10.3390\/jpm16040192","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T08:08:34Z","timestamp":1775030914000},"page":"192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?"],"prefix":"10.3390","volume":"16","author":[{"given":"Stanislovas S.","family":"Jankauskas","sequence":"first","affiliation":[{"name":"School of Medicine, City University of New York, Manhattan, NY 10031, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahimeh","family":"Varzideh","sequence":"additional","affiliation":[{"name":"School of Medicine, City University of New York, Manhattan, NY 10031, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Urna","family":"Kansakar","sequence":"additional","affiliation":[{"name":"School of Medicine, City University of New York, Manhattan, NY 10031, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7231-375X","authenticated-orcid":false,"given":"Gaetano","family":"Santulli","sequence":"additional","affiliation":[{"name":"School of Medicine, City University of New York, Manhattan, NY 10031, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1093\/mind\/LIX.236.433","article-title":"Computing Machinery and Intelligence","volume":"LIX","author":"Turing","year":"1950","journal-title":"Mind"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1056\/NEJM198208193070803","article-title":"Internist-1, an experimental computer-based diagnostic consultant for general internal medicine","volume":"307","author":"Miller","year":"1982","journal-title":"N. Engl. J. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4361","DOI":"10.1038\/s41598-024-55141-2","article-title":"Unraveling atherosclerotic cardiovascular disease risk factors through conditional probability analysis with Bayesian networks: Insights from the AZAR cohort study","volume":"14","author":"Esmaeili","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_4","first-page":"899","article-title":"Prediction of risk of cardiovascular events in patients with mild to moderate coronary artery lesions using naive Bayesian networks","volume":"13","author":"Wang","year":"2016","journal-title":"J. Geriatr. Cardiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.compbiomed.2015.08.015","article-title":"Real-time prediction of acute cardiovascular events using hardware-implemented Bayesian networks","volume":"69","author":"Tylman","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Roberts, J.M., Parlikar, T.A., Heldt, T., and Verghese, G.C. (2006). Bayesian networks for cardiovascular monitoring. 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA, 30 August\u20133 September 2006, IEEE.","DOI":"10.1109\/IEMBS.2006.259985"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/S0933-3657(96)00367-3","article-title":"An evaluation of machine-learning methods for predicting pneumonia mortality","volume":"9","author":"Cooper","year":"1997","journal-title":"Artif. Intell. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/S0933-3657(98)00064-5","article-title":"Two-Stage Machine Learning model for guideline development","volume":"16","author":"Mani","year":"1999","journal-title":"Artif. Intell. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/S0933-3657(98)00063-3","article-title":"Analysing and improving the diagnosis of ischaemic heart disease with machine learning","volume":"16","author":"Kukar","year":"1999","journal-title":"Artif. Intell. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Miramontes, S., Ferguson, E.L., Zimmerman, S., Phelps, E., Oskotsky, B., Oskotsky, T.T., Capra, J.A., Tsoy, E., Sirota, M., and Glymour, M.M. (2026). Social and Cardiovascular Risk Factors as Predictors of the Progression from Mild Cognitive Impairment to Dementia in a Large EHR Database. medRxiv.","DOI":"10.64898\/2026.03.02.26347451"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106702","DOI":"10.1016\/j.jinf.2026.106702","article-title":"Pre-COVID-19 body mass index and post-acute cardiovascular, gastrointestinal, and neuropsychiatric outcomes among children and young adults with SARS-CoV-2 infection: An EHR-based cohort study from the RECOVER Initiative","volume":"92","author":"Zhou","year":"2026","journal-title":"J. Infect."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ryu, H., Lee, M., Kim, S.H., Kim, J.H., and Yang, H.J. (2025). Federated Learning for Cardiovascular Disease Prediction: A Comparative Review of Biosignal- and EHR-Based Approaches. Healthcare, 13.","DOI":"10.3390\/healthcare13212811"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, B., Thacker, D., Zhou, T., Zhang, D., Lei, Y., Chen, J., Chrischilles, E., Christakis, D.A., Fernandez, S., and Garg, V. (2024). Post-Acute Cardiovascular Outcomes of COVID-19 in Children and Adolescents: An EHR Cohort Study from the RECOVER Project. medRxiv.","DOI":"10.1101\/2024.05.14.24307380"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ahj.2023.05.009","article-title":"Generalizability of an EHR-network dataset to the United States for cardiovascular disease conditions: Comparison of Cerner real world data with the national inpatient sample","volume":"263","author":"Shah","year":"2023","journal-title":"Am. Heart J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.34067\/KID.0001572022","article-title":"Effect of Intensive versus Standard BP Control on AKI and Subsequent Cardiovascular Outcomes and Mortality: Findings from the SPRINT EHR Study","volume":"3","author":"Drawz","year":"2022","journal-title":"Kidney360"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104587","DOI":"10.1016\/j.ijmedinf.2021.104587","article-title":"The potential role of EHR data in optimizing eligibility criteria definition for cardiovascular outcome trials","volume":"156","author":"Kim","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100808","DOI":"10.1016\/j.conctc.2021.100808","article-title":"Addressing cancer survivors\u2019 cardiovascular health using the automated heart health assessment (AH-HA) EHR tool: Initial protocol and modifications to address COVID-19 challenges","volume":"22","author":"Foraker","year":"2021","journal-title":"Contemp. Clin. Trials Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100705","DOI":"10.1016\/j.conctc.2021.100705","article-title":"Improving cardiovascular outcomes by using team-supported, EHR-leveraged, active management: Disseminating a successful quality improvement project","volume":"21","author":"Lewinski","year":"2021","journal-title":"Contemp. Clin. Trials Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2624","DOI":"10.1109\/TPAMI.2013.83","article-title":"Distance-based image classification: Generalizing to new classes at near-zero cost","volume":"35","author":"Mensink","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","first-page":"68","article-title":"Leveraging 2D Deep Learning ImageNet-trained models for Native 3D Medical Image Analysis","volume":"13769","author":"Baheti","year":"2023","journal-title":"Brainlesion"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1459107","DOI":"10.1155\/2020\/1459107","article-title":"Generative Adversarial Network Technologies and Applications in Computer Vision","volume":"2020","author":"Jin","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104254","DOI":"10.1016\/j.compbiomed.2021.104254","article-title":"Synthesizing anonymized and labeled TOF-MRA patches for brain vessel segmentation using generative adversarial networks","volume":"131","author":"Kossen","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gonzalez-Juanatey, C., Anguita-Sanchez, M., Barrios, V., Nunez-Gil, I., Gomez-Doblas, J.J., Garcia-Moll, X., Lafuente-Gormaz, C., Rollan-Gomez, M.J., Peral-Disdier, V., and Martinez-Dolz, L. (2022). Major Adverse Cardiovascular Events in Coronary Type 2 Diabetic Patients: Identification of Associated Factors Using Electronic Health Records and Natural Language Processing. J. Clin. Med., 11.","DOI":"10.3390\/jcm11206004"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1038\/s43856-022-00157-w","article-title":"Using deep learning-based natural language processing to identify reasons for statin nonuse in patients with atherosclerotic cardiovascular disease","volume":"2","author":"Sarraju","year":"2022","journal-title":"Commun. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e210085","DOI":"10.1148\/ryai.210085","article-title":"Automatic Diagnosis Labeling of Cardiovascular MRI by Using Semisupervised Natural Language Processing of Text Reports","volume":"4","author":"Zaman","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1002\/clc.23687","article-title":"Natural language processing for the assessment of cardiovascular disease comorbidities: The cardio-Canary comorbidity project","volume":"44","author":"Berman","year":"2021","journal-title":"Clin. Cardiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100289","DOI":"10.1016\/j.patter.2021.100289","article-title":"Structuring clinical text with AI: Old versus new natural language processing techniques evaluated on eight common cardiovascular diseases","volume":"2","author":"Zhan","year":"2021","journal-title":"Patterns"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"S128","DOI":"10.1016\/j.jbi.2015.08.002","article-title":"Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes","volume":"58","author":"Khalifa","year":"2015","journal-title":"J. Biomed. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1161\/HYPERTENSIONAHA.125.25305","article-title":"Large Language Model Agent for Managing Patients with Suspected Hypertension","volume":"83","author":"Wang","year":"2026","journal-title":"Hypertension"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, H., Fan, C., and Liu, C. (2026). Development and validation of a machine learning model to predict comorbid hypertension in patients with type 2 diabetes. Front. Med., 13.","DOI":"10.3389\/fmed.2026.1754916"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"583914","DOI":"10.2147\/JMDH.S583914","article-title":"Smart Community Hypertension Management: A Narrative Review of Promises and Challenges","volume":"19","author":"Yang","year":"2026","journal-title":"J. Multidiscip. Healthc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e81400","DOI":"10.2196\/81400","article-title":"Effects of Artificial Intelligence Recognition-Based Telerehabilitation on Exercise Capacity in Patients with Hypertension: Randomized Controlled Trial","volume":"28","author":"Yao","year":"2026","journal-title":"J. Med. Internet Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1161\/HYPERTENSIONAHA.125.25492","article-title":"Performance of Large Language Models in Analyzing Common Hypertension Scenarios","volume":"83","author":"Zand","year":"2026","journal-title":"Hypertension"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100111","DOI":"10.1016\/j.esmorw.2024.100111","article-title":"Cardiovascular toxicities in cancer patients treated with immune checkpoint inhibitors: Multicenter study using natural language processing on Belgian hospital data","volume":"7","author":"Delombaerde","year":"2025","journal-title":"ESMO Real. World Data Digit. Oncol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Joseph, S., Bhardwaj, A., Skariah, J., Aggarwal, I., Shah, V., and Harris, R.A. (2025). Effects of education level on natural language processing in cardiovascular health communication. Front. Public. Health, 13.","DOI":"10.3389\/fpubh.2025.1688173"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e42774","DOI":"10.2196\/42774","article-title":"Identification of Gender Differences in Acute Myocardial Infarction Presentation and Management at Aga Khan University Hospital-Pakistan: Natural Language Processing Application in a Dataset of Patients with Cardiovascular Disease","volume":"8","author":"Ngaruiya","year":"2024","journal-title":"JMIR Form. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e143","DOI":"10.1002\/hem3.143","article-title":"Development of a natural language processing pipeline for assessment of cardiovascular risk in myeloproliferative neoplasms","volume":"8","author":"Duminuco","year":"2024","journal-title":"Hemasphere"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Iglesias, P., Arias, J., Lopez, G., Romero, I., and Diez, J.J. (2023). Primary Hyperparathyroidism and Cardiovascular Disease: An Association Study Using Clinical Natural Language Processing Systems and Big Data Analytics. J. Clin. Med., 12.","DOI":"10.3390\/jcm12216718"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"109120","DOI":"10.1016\/j.jdiacomp.2025.109120","article-title":"Evaluation of IDx-DR software for diabetic retinopathy screening in outpatient clinics: Efficacy, safety, and feasibility in a real-world setting","volume":"39","author":"Huber","year":"2025","journal-title":"J. Diabetes Complicat."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.ajo.2025.02.022","article-title":"Diagnostic Accuracy of IDX-DR for Detecting Diabetic Retinopathy: A Systematic Review and Meta-Analysis","volume":"273","author":"Khan","year":"2025","journal-title":"Am. J. Ophthalmol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1111\/aos.17428","article-title":"Evaluating the efficacy of AI systems in diabetic retinopathy detection: A comparative analysis of Mona DR and IDx-DR","volume":"103","author":"Grzybowski","year":"2025","journal-title":"Acta Ophthalmol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s40123-024-01049-z","article-title":"Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Software: IDx-DR and RetCAD","volume":"14","author":"Grzybowski","year":"2025","journal-title":"Ophthalmol. Ther."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1159\/000534098","article-title":"Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Softwares: IDx-DR and Medios Artificial Intelligence","volume":"66","author":"Grzybowski","year":"2023","journal-title":"Ophthalmic Res."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Grzybowski, A., and Brona, P. (2021). Analysis and Comparison of Two Artificial Intelligence Diabetic Retinopathy Screening Algorithms in a Pilot Study: IDx-DR and Retinalyze. J. Clin. Med., 10.","DOI":"10.3390\/jcm10112352"},{"key":"ref_45","first-page":"307","article-title":"IDx-DR for Diabetic Retinopathy Screening","volume":"101","author":"Savoy","year":"2020","journal-title":"Am. Fam. Physician"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1111\/aos.13613","article-title":"Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System","volume":"96","author":"Abramoff","year":"2018","journal-title":"Acta Ophthalmol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3551","DOI":"10.1038\/s41598-022-07186-4","article-title":"Federated learning for multi-center imaging diagnostics: A simulation study in cardiovascular disease","volume":"12","author":"Linardos","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Alsulami, M., Yaqoob, M.M., Alsadie, D., Saudagar, A.K.J., AlKhathami, M., and Farooq Khattak, U. (2023). Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence. Diagnostics, 13.","DOI":"10.3390\/diagnostics13142340"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e14830","DOI":"10.2196\/14830","article-title":"Fine-Tuning Bidirectional Encoder Representations from Transformers (BERT)-Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study","volume":"7","author":"Li","year":"2019","journal-title":"JMIR Med. Inf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1056\/NEJMsr2214184","article-title":"Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine","volume":"388","author":"Lee","year":"2023","journal-title":"N. Engl. J. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"133","DOI":"10.32628\/IJSRSET24114107","article-title":"Artificial Intelligence in Healthcare: A Review","volume":"11","author":"Bhagat","year":"2024","journal-title":"Int. J. Sci. Res. Sci. Eng. Technol."},{"key":"ref_52","first-page":"118","article-title":"AI in Healthcare: Revolutionizing Diagnosis and Therapy","volume":"3","author":"Zeb","year":"2024","journal-title":"Int. J. Multidiscip. Sci. Arts"},{"key":"ref_53","first-page":"e77561","article-title":"Current Status and Future of Artificial Intelligence in Medicine","volume":"17","author":"Basubrin","year":"2025","journal-title":"Cureus"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e70312","DOI":"10.1002\/hsr2.70312","article-title":"The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency","volume":"8","author":"Faiyazuddin","year":"2025","journal-title":"Health Sci. Rep."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shah, N.J., and Chen, H. (2024). Optimizing Healthcare Delivery: Investigating Key Areas for AI Integration and Impact in Clinical Settings. preprint.","DOI":"10.20944\/preprints202402.1694.v1"},{"key":"ref_56","first-page":"1","article-title":"AI in Healthcare: Transforming Patient Care Through Predictive Analytics and Decision Support Systems","volume":"1","author":"Rana","year":"2024","journal-title":"J. Artif. Intell. Gen. Sci. JAIGS"},{"key":"ref_57","first-page":"44","article-title":"Personalized Health Monitoring Systems: Integrating Wearable and AI","volume":"16","author":"Secara","year":"2024","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.47392\/IRJAEM.2024.0177","article-title":"Advancing Healthcare Through Artificial Intelligence: Opportunities, Challenges and Future Directions","volume":"2","author":"Vaidya","year":"2024","journal-title":"Int. Res. J. Adv. Engg MGT"},{"key":"ref_59","first-page":"1","article-title":"Revolutionizing Innovations and Impact of Artificial Intelligence in Healthcare","volume":"6","author":"Chatterjee","year":"2024","journal-title":"Int. J. Multidiscip. Res."},{"key":"ref_60","unstructured":"Abatal, A., and Korchi, A. (2023). Transforming Healthcare Systems with Artificial Intelligence: Revolutionizing Efficiency, Quality, and Patient Care. preprint."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Poalelungi, D.G., Mu\u0219at, C.L., Fulga, A., Neagu, M., Neagu, A.I., Piraianu, A.I., and Fulga, I. (2023). Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J. Pers. Med., 13.","DOI":"10.3390\/jpm13081214"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"23779608231196847","DOI":"10.1177\/23779608231196847","article-title":"The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings","volume":"9","author":"Pailaha","year":"2023","journal-title":"Sage Open Nurs."},{"key":"ref_63","first-page":"36","article-title":"The Future of Medicine: Harnessing the Power of AI for Revolutionizing Healthcare","volume":"2","author":"Harry","year":"2023","journal-title":"Int. J. Multidiscip. Sci. Arts"},{"key":"ref_64","first-page":"52","article-title":"The Security Risks of Artificial Intelligence Applications on the Healthcare System","volume":"5","year":"2024","journal-title":"Saudi J. Health Syst. Res."},{"key":"ref_65","first-page":"16","article-title":"Transforming Healthcare: The Impact and Future of Artificial Intelligence in Healthcare","volume":"1","author":"Suryakiran","year":"2023","journal-title":"J. Artfic. Intell. Machine. Lear."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1098\/rsos.241873","article-title":"Ethical and Legal Considerations in Healthcare AI: Innovation and Policy for Safe and Fair Use","volume":"12","author":"Pham","year":"2025","journal-title":"R. Soc. Open Sci."},{"key":"ref_67","first-page":"1","article-title":"Ethical Implications of AI in Healthcare","volume":"8","author":"Dhawan","year":"2024","journal-title":"Int. J. Sci. Res. Eng. Manag."},{"key":"ref_68","first-page":"31","article-title":"AI in Healthcare: Revolutionizing Patient Care with Predictive Analytics and Decision Support Systems","volume":"1","year":"2024","journal-title":"J. Artif. Intell. Gen. Sci. JAIGS"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.58344\/jws.v2i10.448","article-title":"AI\u2019S Healing Touch: Examining Machine Learning\u2019s Transformative Effects on Healthcare","volume":"2","author":"Husnain","year":"2023","journal-title":"J. World Sci."},{"key":"ref_70","first-page":"678","article-title":"Healthcare Leaders\u2019 Perceptions of the Usefulness of AI Applications in Clinical Work: A Qualitative Study","volume":"Volume 302","author":"Petersson","year":"2023","journal-title":"Caring is Sharing\u2014Exploiting the Value in Data for Health and Innovation"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.ijmedinf.2012.03.001","article-title":"Developing healthcare rule-based expert systems: Case study of a heart failure telemonitoring system","volume":"81","author":"Seto","year":"2012","journal-title":"Int. J. Med. Inf."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e007952","DOI":"10.1161\/CIRCEP.119.007952","article-title":"Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology","volume":"13","author":"Feeny","year":"2020","journal-title":"Circ. Arrhythm. Electrophysiol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s13040-024-00381-1","article-title":"Deep learning for automatic calcium detection in echocardiography","volume":"17","author":"Elvas","year":"2024","journal-title":"BioData Min."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Nanayakkara, T., Clermont, G., Langmead, C.J., and Swigon, D. (2022). Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. PLoS Digit. Health, 1.","DOI":"10.1371\/journal.pdig.0000012"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"17","DOI":"10.14797\/mdcvj.1263","article-title":"Emerging Applications for Computer Vision and Artificial Intelligence in Management of the Cardiovascular Patient","volume":"19","author":"Osztrogonacz","year":"2023","journal-title":"Methodist. Debakey Cardiovasc. J."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"108321","DOI":"10.1016\/j.compbiomed.2024.108321","article-title":"Predictive analytics for cardiovascular patient readmission and mortality: An explainable approach","volume":"174","author":"Huberts","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"eadm8233","DOI":"10.1126\/scirobotics.adm8233","article-title":"Robotic manipulation of cardiomyocytes to identify gap junction modifiers for arrhythmogenic cardiomyopathy","volume":"9","author":"Dou","year":"2024","journal-title":"Sci. Robot."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1080\/0142159X.2025.2531812","article-title":"The hallucination of learning with generative artificial intelligence","volume":"47","author":"Sandars","year":"2025","journal-title":"Med. Teach."},{"key":"ref_79","first-page":"340","article-title":"On the \u2018Hallucinations\u2019 of Artificial Intelligence and the Hallucination Experience in Human","volume":"35","author":"Guliyev","year":"2024","journal-title":"Turk. Psikiyatr. Derg."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"68","DOI":"10.51866\/lte.527","article-title":"Hallucination: A key challenge to Artificial Intelligence-Generated writing","volume":"18","author":"Jamaluddin","year":"2023","journal-title":"Malays. Fam. Physician"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e54345","DOI":"10.2196\/54345","article-title":"Reference Hallucination Score for Medical Artificial Intelligence Chatbots: Development and Usability Study","volume":"12","author":"Aljamaan","year":"2024","journal-title":"JMIR Med. Inform."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"133649","DOI":"10.1016\/j.ijcard.2025.133649","article-title":"Generative AI-powered explainable prediction model: Enhancing early in-hospital mortality alert for patients with acute myocardial infarction","volume":"440","author":"Xie","year":"2025","journal-title":"Int. J. Cardiol."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Liu, X., Li, S., Zhu, Q., Xu, S., and Jin, Q. (2025). Interpretable Semi-federated Learning for Multimodal Cardiac Imaging and Risk Stratification: A Privacy-Preserving Framework. J. Imaging Inform. Med.","DOI":"10.1007\/s10278-025-01643-y"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"17927","DOI":"10.1038\/s41598-025-01650-7","article-title":"Predicting cardiovascular risk with hybrid ensemble learning and explainable AI","volume":"15","author":"Shah","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Zamprogno, G., Dietz, E., Heimisch, L., and Russwinkel, N. (2023). A hybrid computational approach to anticipate individuals in sequential problem solving. Front. Artif. Intell., 6.","DOI":"10.3389\/frai.2023.1223251"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1136\/heartjnl-2017-311198","article-title":"Machine Learning in Cardiovascular Medicine: Are We There Yet?","volume":"104","author":"Shameer","year":"2018","journal-title":"Heart"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"163686","DOI":"10.1109\/ACCESS.2021.3133338","article-title":"A Deep Learning Framework Integrating the Spectral and Spatial Features for Image-Assisted Medical Diagnostics","volume":"9","author":"Ghosh","year":"2021","journal-title":"IEEE Access"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41746-024-01028-5","article-title":"The Impact of Using Reinforcement Learning to Personalize Communication on Medication Adherence: Findings from the REINFORCE Trial","volume":"7","author":"Lauffenburger","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Varzideh, F., Pande, S., Jankauskas, S.S., Mone, P., Kansakar, U., and Santulli, G. (2026). Artificial Intelligence in Neurocardiology: Decoding Brain\u2013Heart Network Interactions for Clinical and Translational Insights. Front. Neurosci., in press.","DOI":"10.3389\/fnins.2026.1788653"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"e052091","DOI":"10.1136\/bmjopen-2021-052091","article-title":"REinforcement Learning to Improve Non-Adherence for Diabetes Treatments by Optimising Response and Customising Engagement (REINFORCE): Study Protocol of a Pragmatic Randomised Trial","volume":"11","author":"Lauffenburger","year":"2021","journal-title":"BMJ Open"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Shi, J., and Xiang, M. (2024). Using Ghost Convolution YOLO to Detect Brain Tumor. Proceedings of the 3rd International Symposium on Computer Applications and Information Systems (ISCAIS 2024), SPIE.","DOI":"10.1117\/12.3034790"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"407","DOI":"10.47392\/IRJAEH.2024.0060","article-title":"Applications of Artificial Intelligence in the Medical Field: A Survey","volume":"2","author":"Sivaguru","year":"2024","journal-title":"Int. Res. J. Adv. Eng. Hub."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"561","DOI":"10.22271\/tpi.2019.v8.i3j.25392","article-title":"Explainable AI in Healthcare: Interpretable Deep Learning Models for Disease Diagnosis","volume":"8","author":"Verma","year":"2019","journal-title":"Pharma Innov."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"20552076251375570","DOI":"10.1177\/20552076251375570","article-title":"Federated Learning-Based Virtual Dual-Energy CT Generation from Single-Energy CT for Gout Detection","volume":"11","author":"Dong","year":"2025","journal-title":"Digit. Health"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"109079","DOI":"10.1016\/j.cmpb.2025.109079","article-title":"Precision at heart: An IoT-based vertical federated learning approach for heterogeneous data-driven cardiovascular disease risk prediction","volume":"273","author":"Shajimon","year":"2026","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"qyaf012","DOI":"10.1093\/ehjimp\/qyaf012","article-title":"New ways to use imaging data in cardiovascular research: Survey of opinions on federated learning and synthetic data","volume":"3","author":"Williams","year":"2025","journal-title":"Eur. Heart J.-Imaging Methods Pract."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s12931-023-02607-y","article-title":"Alpha-1-antitrypsin-deficiency is associated with lower cardiovascular risk: An approach based on federated learning","volume":"25","author":"Zoller","year":"2024","journal-title":"Respir. Res."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"143742","DOI":"10.1109\/ACCESS.2019.2941898","article-title":"Big Data Features, Applications, and Analytics in Cardiology\u2014A Systematic Literature Review","volume":"7","author":"Nazir","year":"2019","journal-title":"IEEE Access"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"e38","DOI":"10.2196\/medinform.5359","article-title":"Challenges and Opportunities of Big Data in Health Care: A Systematic Review","volume":"4","author":"Kruse","year":"2016","journal-title":"JMIR Med. Inform."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Counseller, Q., and Aboelkassem, Y. (2023). Recent Technologies in Cardiac Imaging. Front. Med. Technol., 4.","DOI":"10.3389\/fmedt.2022.984492"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"e73846","DOI":"10.2196\/73846","article-title":"AI-Driven Real-Time Monitoring of Cardiovascular Conditions with Wearable Devices: Scoping Review","volume":"13","author":"Abedi","year":"2025","journal-title":"JMIR mHealth uHealth"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"e77911","DOI":"10.2196\/77911","article-title":"Validity of Heart Rate Measurement Using Wearable Devices During Cardiopulmonary Exercise Testing in Patients with Cardiovascular Disease: Prospective Pilot Validation Study","volume":"9","author":"Kitagaki","year":"2025","journal-title":"JMIR Cardio"},{"key":"ref_103","first-page":"c312","article-title":"Correction: Exploring the Role of Wearable Electronic Medical Devices in Improving Cardiovascular Risk Factors and Outcomes Among Adults: A Systematic Review","volume":"17","author":"Seffah","year":"2025","journal-title":"Cureus"},{"key":"ref_104","first-page":"e90208","article-title":"Wearable Devices and AI-Driven Remote Monitoring in Cardiovascular Medicine: A Narrative Review","volume":"17","author":"Gaoudam","year":"2025","journal-title":"Cureus"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"100202","DOI":"10.1016\/j.mcpdig.2025.100202","article-title":"Selecting Wearable Devices to Measure Cardiovascular Functions in Community-Dwelling Adults: Application of a Practical Guide for Device Selection","volume":"3","author":"Lu","year":"2025","journal-title":"Mayo Clin. Proc. Digit. Health"},{"key":"ref_106","first-page":"e83673","article-title":"Role of Mobile Health and Wearable Devices in Cardiovascular Disease Prevention: A Comprehensive Review","volume":"17","author":"Ahmad","year":"2025","journal-title":"Cureus"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.cjco.2025.02.017","article-title":"Wearable Devices for Exercise Prescription and Physical Activity Monitoring in Patients with Various Cardiovascular Conditions","volume":"7","author":"Terada","year":"2025","journal-title":"CJC Open"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"e65782","DOI":"10.2196\/65782","article-title":"Mobile Apps and Wearable Devices for Cardiovascular Health: Narrative Review","volume":"13","author":"Chauhan","year":"2025","journal-title":"JMIR mHealth uHealth"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"17895","DOI":"10.1021\/acsami.4c22895","article-title":"Advancing Sports Cardiology: Integrating Artificial Intelligence with Wearable Devices for Cardiovascular Health Management","volume":"17","author":"Zheng","year":"2025","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Singh, J., Ellingson, C.J., Gagarinova, M., Thakkar, R., Mehta, N., Ahmad, K., Sultana, S., Bhat, S., and Dehghani, P. (2025). Wearable Devices for Hemodynamic Assessment in Cardiovascular Disease: A Short Literature Review. Cardiol. Rev., in press.","DOI":"10.1097\/CRD.0000000000000875"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"108186","DOI":"10.1016\/j.maturitas.2024.108186","article-title":"Wearable devices, ischemic heart disease and cardiovascular outcomes: A systematic review and meta-analysis","volume":"193","author":"Ballavenuto","year":"2025","journal-title":"Maturitas"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Iqbal, S.M.A., Leavitt, M.A., Mahgoub, I., and Asghar, W. (2024). Advances in Cardiovascular Wearable Devices. Biosensors, 14.","DOI":"10.3390\/bios14110525"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"e2316634","DOI":"10.1001\/jamanetworkopen.2023.16634","article-title":"Use of Wearable Devices in Individuals with or at Risk for Cardiovascular Disease in the US, 2019 to 2020","volume":"6","author":"Dhingra","year":"2023","journal-title":"JAMA Netw. Open"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Park, U., Miyagi, M., Wu, X., Ito, M., Chikai, M., Sakai, F., Miura, T., Sato, H., Murai, A., and Freeman, S. (2026). Digitalization of Comprehensive Geriatric Assessments for Nursing Practice: A Feasibility and Proof-of-Concept Study Toward Nursing Home Implementation. Healthcare, 14.","DOI":"10.3390\/healthcare14040528"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1161\/CIRCRESAHA.122.322389","article-title":"Wearable Devices in Cardiovascular Medicine","volume":"132","author":"Hughes","year":"2023","journal-title":"Circ. Res."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1089\/tmj.2022.0280","article-title":"The Impact of Consumer Wearable Devices on Physical Activity and Adherence to Physical Activity in Patients with Cardiovascular Disease: A Systematic Review of Systematic Reviews and Meta-Analyses","volume":"29","author":"Alam","year":"2023","journal-title":"Telemed. J. e-Health"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1038\/s41569-022-00729-2","article-title":"Shaping the future of cardiovascular medicine in the new era of wearable devices","volume":"19","author":"Gehr","year":"2022","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"S93","DOI":"10.3349\/ymj.2022.63.S93","article-title":"Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis","volume":"63","author":"Lee","year":"2022","journal-title":"Yonsei Med. J."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1089\/tmj.2021.0347","article-title":"Cardiovascular Disease Risk Stratification in Wrist Wearable Devices and e-Health App Users: A Large-Scale Retrospective Study","volume":"28","author":"Chen","year":"2022","journal-title":"Telemed. J. E Health"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"13921","DOI":"10.1007\/s00521-021-06219-9","article-title":"Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: A deep learning approach","volume":"35","author":"Tan","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1038\/s41569-021-00522-7","article-title":"Smart wearable devices in cardiovascular care: Where we are and how to move forward","volume":"18","author":"Bayoumy","year":"2021","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1146\/annurev-med-050919-031534","article-title":"Wearable Devices to Monitor and Reduce the Risk of Cardiovascular Disease: Evidence and Opportunities","volume":"72","author":"Mizuno","year":"2021","journal-title":"Annu. Rev. Med."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"e2002878","DOI":"10.1002\/adma.202002878","article-title":"Holistically Engineered Polymer-Polymer and Polymer-Ion Interactions in Biocompatible Polyvinyl Alcohol Blends for High-Performance Triboelectric Devices in Self-Powered Wearable Cardiovascular Monitorings","volume":"32","author":"Wang","year":"2020","journal-title":"Adv. Mater."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s13142-016-0454-0","article-title":"Adherence with physical activity monitoring wearable devices in a community-based population: Observations from the Washington, D.C., Cardiovascular Health and Needs Assessment","volume":"7","author":"Yingling","year":"2017","journal-title":"Transl. Behav. Med."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"5648","DOI":"10.1177\/00031348231167397","article-title":"The Not-So-Distant Future or Just Hype? Utilizing Machine Learning to Predict 30-Day Post-Operative Complications in Laparoscopic Colectomy Patients","volume":"89","author":"Velmahos","year":"2023","journal-title":"Am. Surg."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"20","DOI":"10.29019\/enfoqueute.1023","article-title":"Current Status and Challenges of IoT Research in the Ecuadorian Healthcare Sector: A Systematic Literature Review","volume":"15","author":"Orellana","year":"2024","journal-title":"Enfoque UTE"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"4448","DOI":"10.1002\/ehf2.12962","article-title":"Study Design of Heart Failure Events Reduction with Remote Monitoring and eHealth Support (HERMeS)","volume":"7","author":"Yun","year":"2020","journal-title":"Esc. Heart Fail."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1017\/S1047951121004406","article-title":"Provider Insights on Shared Decision-Making with Families Affected by CHD","volume":"32","author":"Pinto","year":"2021","journal-title":"Cardiol. Young"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"e2020011833","DOI":"10.1542\/peds.2020-011833","article-title":"Machine Learning for Child and Adolescent Health: A Systematic Review","volume":"147","author":"Hoodbhoy","year":"2020","journal-title":"Pediatrics"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Kirkham, E., Crompton, C.J., Iveson, M.H., Beange, I., McIntosh, A.M., and Fletcher-Watson, S. (2021). Co-Development of a Best Practice Checklist for Mental Health Data Science: A Delphi Study. Front. Psychiatry, 12.","DOI":"10.3389\/fpsyt.2021.643914"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1177\/2047487318814970","article-title":"Patterns of Treatment with Antiplatelet Therapy After an Acute Coronary Syndrome: Data from a Large Database in a Community Setting","volume":"26","author":"Cimminiello","year":"2018","journal-title":"Eur. J. Prev. Cardiol."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Portalis, M.R., Cymberknop, L.J., Farro, I., and Armentano, R.L. (2023). Computational Clustering Reveals Differentiated Coronary Artery Calcium Progression at Prevalent Levels of Pulse Wave Velocity by Classifying High-Risk Patients. Front. Cardiovasc. Med., 10.","DOI":"10.3389\/fcvm.2023.1161914"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"100574","DOI":"10.1016\/j.jacadv.2023.100574","article-title":"Echocardiography in the Era of Artificial Intelligence","volume":"2","author":"Silversides","year":"2023","journal-title":"JACC Adv."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1016\/j.echo.2022.04.005","article-title":"Future Guidelines for Artificial Intelligence in Echocardiography","volume":"35","author":"Tseng","year":"2022","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"e017436","DOI":"10.1161\/CIRCIMAGING.124.017436","article-title":"Artificial Intelligence-Enhanced Analysis of Echocardiography-Based Radiomic Features for Myocardial Hypertrophy Detection and Etiology Differentiation","volume":"18","author":"Moon","year":"2025","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.pcad.2023.09.001","article-title":"Artificial intelligence in cardiac computed tomography","volume":"81","author":"Aromiwura","year":"2023","journal-title":"Prog. Cardiovasc. Dis."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1161\/CIRCIMAGING.121.013025","article-title":"Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review","volume":"14","author":"Infante","year":"2021","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1093\/ehjci\/jeaf144","article-title":"Has artificial intelligence proven stress perfusion cardiac computed tomography obsolete or more relevant than ever?","volume":"26","author":"Shatla","year":"2025","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"132979","DOI":"10.1016\/j.ijcard.2025.132979","article-title":"Artificial intelligence for left ventricular hypertrophy detection and differentiation on echocardiography, cardiac magnetic resonance and cardiac computed tomography: A systematic review","volume":"422","author":"Cirillo","year":"2025","journal-title":"Int. J. Cardiol."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Abu Suleiman, A., Russo, F., Della Valle, L., Ausiello, D., Bukowska-Olech, E., Iannibelli, V., Al Droubi, M.O., Sannino, G., Bernardi, M., and Spadafora, L. (2025). Cardiac Computed Tomography for the Assessment of Myocardial Bridging: A Scoping Review of the Emerging Role of Artificial Intelligence and Machine Learning. J. Cardiovasc. Dev. Dis., 12.","DOI":"10.20944\/preprints202506.0085.v1"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"100935","DOI":"10.1016\/j.phro.2026.100935","article-title":"Comparative analysis of artificial intelligence-based contouring of cardiac substructures on computed tomography scans for radiation therapy","volume":"38","author":"Moignier","year":"2026","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"qyaf121","DOI":"10.1093\/ehjimp\/qyaf121","article-title":"Artificial intelligence-based bi-ventricular systolic and diastolic volume, ejection fraction using non-contrast ECG-gated cardiac computed tomography","volume":"3","author":"Chao","year":"2025","journal-title":"Eur. Heart J.-Imaging Methods Pract."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"112323","DOI":"10.1016\/j.ejrad.2025.112323","article-title":"Validation of artificial intelligence software for automatic calcium scoring in cardiac and chest computed tomography","volume":"191","author":"Hamelink","year":"2025","journal-title":"Eur. J. Radiol."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.17305\/bb.2024.10497","article-title":"Artificial intelligence-assisted measurements of coronary computed tomography angiography parameters such as stenosis, flow reserve, and fat attenuation for predicting major adverse cardiac events in patients with coronary arterial disease","volume":"24","author":"Luo","year":"2024","journal-title":"Biomol. Biomed."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1097\/RTI.0000000000000765","article-title":"The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography with Different Reconstruction Kernels","volume":"39","author":"Lin","year":"2024","journal-title":"J. Thorac. Imaging"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Bui, V., Hsu, L.Y., Chang, L.C., Sun, A.Y., Tran, L., Shanbhag, S.M., Zhou, W., Mehta, N.N., and Chen, M.Y. (2022). DeepHeartCT: A fully automatic artificial intelligence hybrid framework based on convolutional neural network and multi-atlas segmentation for multi-structure cardiac computed tomography angiography image segmentation. Front. Artif. Intell., 5.","DOI":"10.3389\/frai.2022.1059007"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.jcct.2022.07.005","article-title":"Great debates in cardiac computed tomography: OPINION: \u201cArtificial intelligence and the future of cardiovascular CT\u2014Managing expectation and challenging hype\u201d","volume":"17","author":"Nicol","year":"2023","journal-title":"J. Cardiovasc. Comput. Tomogr."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.jcct.2022.07.004","article-title":"Great debates in cardiac computed tomography: OPINION: \u201cArtificial intelligence is key to the future of CCTA\u2014The great hope\u201d","volume":"17","author":"Motwani","year":"2023","journal-title":"J. Cardiovasc. Comput. Tomogr."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.cpet.2021.06.011","article-title":"Artificial Intelligence and Cardiac PET\/Computed Tomography Imaging","volume":"17","author":"Miller","year":"2022","journal-title":"PET Clin."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1038\/s41591-024-02971-2","article-title":"Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging","volume":"30","author":"Wang","year":"2024","journal-title":"Nat. Med."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"131383","DOI":"10.1016\/j.ijcard.2023.131383","article-title":"Improved assessment of left ventricular ejection fraction using artificial intelligence in echocardiography: A comparative analysis with cardiac magnetic resonance imaging","volume":"394","author":"Sveric","year":"2024","journal-title":"Int. J. Cardiol."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"20210852","DOI":"10.1259\/bjr.20210852","article-title":"Artificial intelligence can detect left ventricular dilatation on contrast-enhanced thoracic computer tomography relative to cardiac magnetic resonance imaging","volume":"95","author":"Asif","year":"2022","journal-title":"Br. J. Radiol."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"S21","DOI":"10.1016\/j.cvdhj.2021.11.005","article-title":"Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging-A systematic review","volume":"2","author":"Jathanna","year":"2021","journal-title":"Cardiovasc. Digit. Health J."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10554-025-03596-3","article-title":"Assessing the robustness of an artificial intelligence segmentation model for quantitative cardiovascular magnetic resonance imaging across cardiac phenotypes","volume":"42","author":"Saad","year":"2026","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Fu, L., Zhang, P., Cheng, L., Zhi, P., Xu, J., Liu, X., Zhang, Y., Xu, Z., and He, K. (2025). An Innovative Artificial Intelligence Classification Model for Non-Ischemic Cardiomyopathy Utilizing Cardiac Biomechanics Derived from Magnetic Resonance Imaging. Bioengineering, 12.","DOI":"10.3390\/bioengineering12060670"},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1016\/j.echo.2023.05.015","article-title":"Validation of American Society of Echocardiography Guideline-Recommended Parameters of Right Ventricular Dysfunction Using Artificial Intelligence Compared with Cardiac Magnetic Resonance Imaging","volume":"36","author":"Hsia","year":"2023","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"1732915","DOI":"10.1155\/2022\/1732915","article-title":"Efficacy of Morphine Combined with Mechanical Ventilation in the Treatment of Heart Failure with Cardiac Magnetic Resonance Imaging under Artificial Intelligence Algorithms","volume":"2022","author":"Geng","year":"2022","journal-title":"Contrast Media Mol. Imaging"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1097\/RTI.0000000000000584","article-title":"Potential Role of Artificial Intelligence in Cardiac Magnetic Resonance Imaging: Can It Help Clinicians in Making a Diagnosis?","volume":"36","author":"Cau","year":"2021","journal-title":"J. Thorac. Imaging"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"5493","DOI":"10.12659\/MSM.916690","article-title":"Evaluation of Early Gadolinium Enhancement (EGE) and Cardiac Functional Parameters in Cine-Magnetic Resonance Imaging (MRI) on Artificial Intelligence in Patients with Acute Myocarditis: A Case-Controlled Observational Study","volume":"25","author":"Yuan","year":"2019","journal-title":"Med. Sci. Monit."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"101773","DOI":"10.1016\/j.nuclcard.2023.101773","article-title":"I see, therefore I am? The value of artificial intelligence in nuclear imaging-based identification of cardiac amyloidosis","volume":"33","author":"Kersting","year":"2024","journal-title":"J. Nucl. Cardiol."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1016\/j.jcmg.2025.09.009","article-title":"Artificial Intelligence Lifts the Fog in SPECT Myocardial Perfusion Imaging with Some Shadows Remaining","volume":"18","year":"2025","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1016\/j.jcmg.2021.04.030","article-title":"Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease","volume":"15","author":"Otaki","year":"2022","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1007\/s00259-025-07145-x","article-title":"Artificial intelligence-powered coronary artery disease diagnosis from SPECT myocardial perfusion imaging: A comprehensive deep learning study","volume":"52","author":"Hajianfar","year":"2025","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.1007\/s12350-018-1432-3","article-title":"Diagnostic performance of an artificial intelligence-driven cardiac-structured reporting system for myocardial perfusion SPECT imaging","volume":"27","author":"Garcia","year":"2020","journal-title":"J. Nucl. Cardiol."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"4","DOI":"10.2967\/jnumed.122.265000","article-title":"Artificial Intelligence for PET and SPECT Image Enhancement","volume":"65","author":"Balaji","year":"2024","journal-title":"J. Nucl. Med."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1007\/s00259-021-05341-z","article-title":"Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT\/CT, PET\/CT, and cardiac CT","volume":"48","author":"Slart","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1109\/JBHI.2020.2991043","article-title":"AI in Medical Imaging Informatics: Current Challenges and Future Directions","volume":"24","author":"Panayides","year":"2020","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"100553","DOI":"10.1016\/j.xgen.2024.100553","article-title":"Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning","volume":"4","author":"Gao","year":"2024","journal-title":"Cell Genom."},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Amal, S., Sawyer, D., and Konik, A. (2024). Editorial: Artificial intelligence and multimodal medical imaging data fusion for improving cardiovascular disease care. Front. Radiol., 4.","DOI":"10.3389\/fradi.2024.1412404"},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Shiokawa, N., Izumo, M., Shimamura, T., Kurosaka, Y., Sat\u00f4, Y., Okamura, T., and Akashi, Y.J. (2024). Accuracy and Efficacy of Artificial Intelligence-Derived Automatic Measurements of Transthoracic Echocardiography in Routine Clinical Practice. J. Clin. Med., 13.","DOI":"10.21203\/rs.3.rs-3894409\/v1"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1007\/s10554-023-02941-8","article-title":"Evaluation of an Artificial Intelligence-Based System for Echocardiographic Estimation of Right Atrial Pressure","volume":"39","author":"Zamzmi","year":"2023","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"259","DOI":"10.5455\/medarh.2022.76.259-266","article-title":"The Reliability of Automated Three-Dimensional Echocardiography-HeartModelA.I. Versus 2D Echocardiography Simpson Methods in Evaluation of Left Ventricle Volumes and Ejection Fraction in Patients with Left Ventricular Dysfunction","volume":"76","author":"Naser","year":"2022","journal-title":"Med. Arch."},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Bisignani, G., Volpe, L., Madeo, A., Vico, R., Bencardino, D., and Bonis, S.D. (2025). AI-Assisted LVEF Assessment Using a Handheld Ultrasound Device: A Single-Center Comparative Study Against Cardiac Magnetic Resonance Imaging. J. Clin. Med., 14.","DOI":"10.3390\/jcm14134708"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1111\/echo.15475","article-title":"Artificial Intelligence for Automated Evaluation of Aortic Measurements in 2D Echocardiography: Feasibility, Accuracy, and Reproducibility","volume":"39","author":"Fukson","year":"2022","journal-title":"Echocardiography"},{"key":"ref_175","first-page":"00592","article-title":"Artificial Intelligence-Based Echocardiography Assessment to Detect Pulmonary Hypertension","volume":"11","author":"Salehi","year":"2024","journal-title":"ERJ Open Res."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"331","DOI":"10.2169\/internalmedicine.4171-24","article-title":"Transforming Echocardiography: The Role of Artificial Intelligence in Enhancing Diagnostic Accuracy and Accessibility","volume":"64","author":"Kusunose","year":"2025","journal-title":"Intern. Med."},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Chen, W., Wu, J., Zhang, Z., Gao, Z., Chen, X., Zhang, Y., Zhou, L., Tang, Z., Yu, W., and Fan, S.-m. (2024). Artificial Intelligence-Assisted Echocardiographic Monitoring in Pediatric Patients on Extracorporeal Membrane Oxygenation. Front. Cardiovasc. Med., 11.","DOI":"10.3389\/fcvm.2024.1418741"},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Ono, S., Komatsu, M., Sakai, A., Arima, H., Ochida, M., Aoyama, R., Yasutomi, S., Asada, K., Kaneko, S., and Sasano, T. (2022). Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning. Biomedicines, 10.","DOI":"10.3390\/biomedicines10051082"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"2401128","DOI":"10.1183\/13993003.01128-2024","article-title":"Emerging Multimodality Imaging Techniques for the Pulmonary Circulation","volume":"64","author":"Rajagopal","year":"2024","journal-title":"Eur. Respir. J."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"141","DOI":"10.31662\/jmaj.2024-0180","article-title":"AI in Echocardiography: State-of-the-Art Automated Measurement Techniques and Clinical Applications","volume":"8","author":"Hirata","year":"2025","journal-title":"JMA J."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"41","DOI":"10.4103\/ajm.AJM_209_17","article-title":"Visual Versus Fully Automated Assessment of Left Ventricular Ejection Fraction","volume":"8","author":"Abazid","year":"2018","journal-title":"Avicenna J. Med."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"e70065","DOI":"10.1111\/echo.70065","article-title":"Prognostic Value of Advanced Echocardiography in Patients with Ischemic Heart Disease: A Comprehensive Review","volume":"42","author":"Zadeh","year":"2024","journal-title":"Echocardiography"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1530\/ERP-14-0051","article-title":"Three-Dimensional Echocardiography in a Dynamic Heart Phantom: Comparison of Five Different Methods to Measure Chamber Volume Using a Commercially Available Software","volume":"1","author":"Wood","year":"2014","journal-title":"Echo Res. Pract."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.ultrasmedbio.2017.09.002","article-title":"Automatic Measurements of Mitral Annular Plane Systolic Excursion and Velocities to Detect Left Ventricular Dysfunction","volume":"44","author":"Grue","year":"2018","journal-title":"Ultrasound Med. Biol."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1111\/anae.12857","article-title":"The Utility of Intra-Operative Three-Dimensional Transoesophageal Echocardiography for Dynamic Measurement of Stroke Volume","volume":"70","author":"Suehiro","year":"2014","journal-title":"Anaesthesia"},{"key":"ref_186","doi-asserted-by":"crossref","unstructured":"Kadoglou, N.P.E., Khattab, E., Velidakis, N., Gkougkoudi, E., and Myrianthefs, M.M. (2024). The Role of Echocardiography in the Diagnosis and Prognosis of Pulmonary Hypertension. J. Pers. Med., 14.","DOI":"10.20944\/preprints202403.1194.v1"},{"key":"ref_187","doi-asserted-by":"crossref","unstructured":"Bourazana, A., Xanthopoulos, A., Briasoulis, A., Magouliotis, D.E., Spiliopoulos, K., Athanasiou, T., Vassilopoulos, G., Skoularigis, J., and Triposkiadis, F. (2024). Artificial Intelligence in Heart Failure: Friend or Foe?. Life, 14.","DOI":"10.3390\/life14010145"},{"key":"ref_188","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, Z., Kahn, A.M., Kligerman, S., Narayan, H.K., and Contijoch, F. (2022). Deep Learning Automates Detection of Wall Motion Abnormalities via Measurement of Longitudinal Strain from ECG-gated CT Images. Front. Cardiovasc. Med., 9.","DOI":"10.3389\/fcvm.2022.1009445"},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"155","DOI":"10.18772\/26180197.2023.v5n3a3","article-title":"Comparison of Chronic Rheumatic Mitral Regurgitation Severity Between Cardiac Magnetic Resonance Imaging and Echocardiography","volume":"5","author":"Meel","year":"2023","journal-title":"Wits J. Clin. Med."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"e045637","DOI":"10.1161\/JAHA.125.045637","article-title":"Artificial Intelligence-Based Automated Echocardiographic Analysis and the Workflow of Sonographers: A Randomized Crossover Trial (AI-Echo RCT)","volume":"15","author":"Sakamoto","year":"2026","journal-title":"J Am Heart Assoc."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1111\/echo.12928","article-title":"Accuracy of Echocardiographic Cardiac Index Assessment in Subjects with Preserved Left Ventricular Ejection Fraction","volume":"32","author":"Maeder","year":"2015","journal-title":"Echocardiography"},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1136\/heartjnl-2021-319725","article-title":"Artificial Intelligence for the Echocardiographic Assessment of Valvular Heart Disease","volume":"108","author":"Nedadur","year":"2022","journal-title":"Heart"},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1161\/ATVBAHA.113.301312","article-title":"Quantitative Assessment of Aortic Elasticity with Aging Using Velocity-Vector Imaging and Its Histologic Correlation","volume":"33","author":"Kim","year":"2013","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"17268","DOI":"10.1038\/s41598-018-35572-4","article-title":"Defining Coronary Flow Patterns: Comprehensive Automation of Transthoracic Doppler Coronary Blood Flow","volume":"8","author":"Sunyecz","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"Dahal, L., Kafle, A., and Khanal, B. (2020). Uncertainty Estimation in Deep 2D Echocardiography Segmentation. arXiv.","DOI":"10.46405\/ejms.v2i2.88"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"e009727","DOI":"10.1161\/CIRCIMAGING.119.009727","article-title":"Combining Artificial Intelligence with Human Insight to Automate Echocardiography","volume":"12","author":"Leeson","year":"2019","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jchf.2024.08.012","article-title":"Automated Identification of Heart Failure with Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing","volume":"13","author":"Nargesi","year":"2025","journal-title":"JACC Heart Fail."},{"key":"ref_198","doi-asserted-by":"crossref","unstructured":"Cersosimo, A., Zito, E., Pierucci, N., Matteucci, A., and Fazia, V.M.L. (2025). A Talk with ChatGPT: The Role of Artificial Intelligence in Shaping the Future of Cardiology and Electrophysiology. J. Pers. Med., 15.","DOI":"10.3390\/jpm15050205"},{"key":"ref_199","doi-asserted-by":"crossref","unstructured":"Menezes Junior, A.D.S., e Silva, A.L.F., e Silva, L.R.F., de Lima, K.B.A., and Oliveira, H.L.D. (2024). A Scoping Review of the Use of Artificial Intelligence in the Identification and Diagnosis of Atrial Fibrillation. J. Pers. Med., 14.","DOI":"10.3390\/jpm14111069"},{"key":"ref_200","doi-asserted-by":"crossref","unstructured":"Dadon, Z., Rav, M., Orlev, A., Carasso, S., Glikson, M., Gottlieb, S., and Alpert, E.A. (2024). Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction. Diagnostics, 14.","DOI":"10.3390\/diagnostics14070767"},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"e1530","DOI":"10.1002\/widm.1530","article-title":"Artificial Intelligence for Atrial Fibrillation Detection, Prediction, and Treatment: A Systematic Review of the Last Decade (2013\u20132023)","volume":"14","author":"Salvi","year":"2024","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1080\/00015385.2025.2576441","article-title":"Belgian position paper on implementing artificial intelligence in cardiology: A roadmap from theory to clinical practice","volume":"81","author":"Dujardin","year":"2026","journal-title":"Acta Cardiol."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"e31230","DOI":"10.2196\/31230","article-title":"Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study","volume":"6","author":"Santala","year":"2022","journal-title":"JMIR Cardio"},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1093\/ehjdh\/ztab080","article-title":"ECG-AI: Electrocardiographic Artificial Intelligence Model for Prediction of Heart Failure","volume":"2","author":"Butler","year":"2021","journal-title":"Eur. Heart J.-Digit. Health"},{"key":"ref_205","doi-asserted-by":"crossref","unstructured":"Nolin-Lapalme, A., Sowa, A., Delfrate, J., Tastet, O., Corbin, D., Kulbay, M., Ozdemir, D., Noel, M.A., Marois-Blanchet, F.-C., and Harvey, F. (2025). Foundation Models for Generalizable Electrocardiogram Interpretation: Comparison of Supervised and Self-Supervised Electrocardiogram Foundation Models. medRxiv.","DOI":"10.1101\/2025.03.02.25322575"},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"025277","DOI":"10.1088\/2631-8695\/addd65","article-title":"Efficient Detection of Cardiac Arrhythmias Using Low-Latency Fpga-Accelerated Hybrid Deep Learning Models","volume":"7","author":"Sekhar","year":"2025","journal-title":"Eng. Res. Express"},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"13","DOI":"10.31071\/kit2019.15.01","article-title":"Modern Approaches to Detecting and Removing Artifacts from EEG Signals. Overview","volume":"14","author":"Redka","year":"2019","journal-title":"Klin. Inform. Telemed."},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"L\u00fc, L., Zhu, T., Ribeiro, A.H., Clifton, L., Zhao, E., Ribeiro, A.L.P., Zhang, Y.T., and Clifton, D.A. (2022). Knowledge Discovery with Electrocardiography Using Interpretable Deep Neural Networks. medRxiv.","DOI":"10.1101\/2022.11.01.22281722"},{"key":"ref_209","first-page":"e89743","article-title":"Investigating the Efficacy of AI-Powered Innovations in ECG Analysis and Continuous Heart Monitoring: A Comprehensive Narrative Review","volume":"17","author":"Revuri","year":"2025","journal-title":"Cureus"},{"key":"ref_210","doi-asserted-by":"crossref","unstructured":"Foti, R., Silvestro, M., Campisi, G., Conti, S., Picciolo, G., Cardi, C., Barbanti, M., Rapisarda, G., Parlavecchio, A., and Sgarito, G. (2025). Continuous Monitoring with Implantable Loop Recorders After Cryoballoon Ablation: Impact on Atrial Fibrillation Recurrence and Therapeutic Management in Era of Artificial Intelligence. J. Clin. Med., 14.","DOI":"10.3390\/jcm14092932"},{"key":"ref_211","doi-asserted-by":"crossref","unstructured":"Iftikhar, H. (2025). AI-Driven Pharmacovigilance and Molecular Profiling of Fluoroquinolone-Associated Cardiotoxicity in the UAE: A Geospatial and Machine Learning Analysis with Structural Modification Strategies (2018\u20132023). medRxiv.","DOI":"10.2139\/ssrn.5253214"},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"90","DOI":"10.46310\/tjim.1559779","article-title":"From Traditional to Modern: A Narrative Review of AI-Based Approaches of Cardiac Arrhythmia Diagnosis","volume":"7","author":"Gupta","year":"2025","journal-title":"Turk. J. Intern. Med."},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.ijcard.2015.04.124","article-title":"Utility of Both Carotid Intima\u2013media Thickness and Endothelial Function for Cardiovascular Risk Stratification in Patients with Angina-Like Symptoms","volume":"190","author":"Matsuzawa","year":"2015","journal-title":"Int. J. Cardiol."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"12250","DOI":"10.53730\/ijhs.v6nS2.8239","article-title":"Cardiovascular Disease Risk Assessment in a Rural Population (&Gt;40 Years)","volume":"6","author":"Agrawal","year":"2022","journal-title":"Int. J. Health Sci."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"177","DOI":"10.29271\/jcpsp.2021.02.177","article-title":"Predictive Value of Betatrophin for Determining the Cardiovascular Risks of Obese Individuals","volume":"31","year":"2021","journal-title":"J. Coll. Physicians Surg. Pak."},{"key":"ref_216","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Mart\u00edn, E., Gopegui, E.R., Le\u00f3n-Latre, M., Ot\u00edn, S., Altemir, I., Polo, V., Poves, J.M.L., Cipr\u00e9s, M., Casasnovas, J.A., and Pablo, L.E. (2017). Influence of Cardiovascular Condition on Retinal and Retinal Nerve Fiber Layer Measurements. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0189929"},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"e200486","DOI":"10.1148\/ryct.2021200486","article-title":"Prediction of Coronary Artery Calcium and Cardiovascular Risk on Chest Radiographs Using Deep Learning","volume":"3","author":"Kamel","year":"2021","journal-title":"Radiol. Cardiothorac. Imaging"},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"e13389","DOI":"10.1111\/ijcp.13389","article-title":"Machine Learning to Predict Cardiovascular Risk","volume":"73","author":"Quesada","year":"2019","journal-title":"Int. J. Clin. Pract."},{"key":"ref_219","doi-asserted-by":"crossref","unstructured":"Wallisch, C., Agibetov, A., Dunkler, D., Haller, M., Samwald, M., Dorffner, G., and Heinze, G. (2021). The Roles of Predictors in Cardiovascular Risk Models\u2014A Question of Modeling Culture?. BMC Med. Res. Methodol., 21.","DOI":"10.1186\/s12874-021-01487-4"},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1093\/eurjpc\/zwad055","article-title":"Determining Cardiovascular Risk in Patients with Unattributed Chest Pain in UK Primary Care: An Electronic Health Record Study","volume":"30","author":"Jordan","year":"2023","journal-title":"Eur. J. Prev. Cardiol."},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"531","DOI":"10.15171\/ijhpm.2015.88","article-title":"Estimation of the Cardiovascular Risk Using World Health Organization\/International Society of Hypertension (WHO\/ISH) Risk Prediction Charts in a Rural Population of South India","volume":"4","author":"Ghorpade","year":"2015","journal-title":"Int. J. Health Policy Manag."},{"key":"ref_222","doi-asserted-by":"crossref","unstructured":"Varzideh, F., Mone, P., Kansakar, U., Pande, S., Jankauskas, S.S., and Santulli, G. (2026). Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension. Hypertension, in press.","DOI":"10.1161\/HYPERTENSIONAHA.126.26094"},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1093\/ajh\/hpaf035","article-title":"Artificial Intelligence to Improve Blood Pressure Control: A State-of-the-Art Review","volume":"38","author":"Karnik","year":"2025","journal-title":"Am. J. Hypertens."},{"key":"ref_224","first-page":"e93","article-title":"Moving Toward Improved Access to Medicines and Health Technologies for Cardiovascular Disease","volume":"47","author":"Lim","year":"2023","journal-title":"Rev. Panam. Salud P\u00fablica"},{"key":"ref_225","doi-asserted-by":"crossref","unstructured":"Psounis, K., Andreadis, E.A., Oikonomaki, T., Roumeliotis, S., Margellos, V., Thodis, E., Passadakis, P., and Panagoutsos, S. (2023). The Prognostic Role of Automated Office Blood Pressure Measurement in Hypertensive Patients with Chronic Kidney Disease. Healthcare, 11.","DOI":"10.3390\/healthcare11101360"},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"d286","DOI":"10.1136\/bmj.d286","article-title":"Conventional Versus Automated Measurement of Blood Pressure in Primary Care Patients with Systolic Hypertension: Randomised Parallel Design Controlled Trial","volume":"342","author":"Myers","year":"2011","journal-title":"BMJ"},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1111\/jch.13748","article-title":"Automated Office Blood Pressure Measurements Obtained with and without Preceding Rest Are Associated with Awake Ambulatory Blood Pressure","volume":"22","author":"Andreadis","year":"2019","journal-title":"J. Clin. Hypertens."},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1097\/MBP.0000000000000683","article-title":"Validation of the Wellvii VitalDetect Blood Pressure Monitor in General Population According to the International Standardization Organization 81060-2:2018","volume":"29","author":"Li","year":"2023","journal-title":"Blood Press. Monit."},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1186\/s12938-016-0266-y","article-title":"Association of Simultaneously Measured Four-Limb Blood Pressures with Cardiovascular Function: A Cross-Sectional Study","volume":"15","author":"Song","year":"2016","journal-title":"Biomed. Eng. Online"},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1097\/MBP.0000000000000686","article-title":"Validation of the ANDON KD-595 Automated Upper-Arm Blood Pressure Monitor According to the Universal Standard","volume":"29","author":"Liu","year":"2023","journal-title":"Blood Press. Monit."},{"key":"ref_231","doi-asserted-by":"crossref","first-page":"e70070","DOI":"10.1111\/jch.70070","article-title":"Analysis of the Accuracy of Unattended Automated Blood Pressure Measurements and Control of Blood Pressure Thresholds: A Multicenter Cross-Sectional Study","volume":"27","author":"Yang","year":"2025","journal-title":"J. Clin. Hypertens."},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1038\/s41371-018-0153-z","article-title":"Accuracy of Automated Blood Pressure Measurements in the Presence of Atrial Fibrillation: Systematic Review and Meta-Analysis","volume":"33","author":"Clark","year":"2019","journal-title":"J. Hum. Hypertens."},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"2074","DOI":"10.1097\/HJH.0b013e32835850d7","article-title":"Automated Blood Pressure Measurement in Atrial Fibrillation","volume":"30","author":"Stergiou","year":"2012","journal-title":"J. Hypertens."},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1161\/HYPERTENSIONAHA.121.17876","article-title":"Impact of 30- Versus 60-Second Time Intervals Between Automated Office Blood Pressure Measurements on Measured Blood Pressure","volume":"78","author":"Juraschek","year":"2021","journal-title":"Hypertension"},{"key":"ref_235","doi-asserted-by":"crossref","first-page":"16444","DOI":"10.14260\/jemds\/2015\/2439","article-title":"Comparison of Automated Non\u2014Invasive Blood Pressure Measurement vs. Manual Non\u2014Invasive Blood Pressure Measurement Among Patients Coming for Pre-Anaesthetic Evaluation","volume":"4","author":"Venkatakrishnan","year":"2015","journal-title":"J. Evol. Med. Dent. Sci."},{"key":"ref_236","first-page":"98","article-title":"The Accuracy of Blood Pressure Measurement","volume":"30","author":"Mesquita","year":"2017","journal-title":"Int. J. Cardiovasc. Sci."},{"key":"ref_237","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1161\/HYPERTENSIONAHA.116.08553","article-title":"Accuracy of Automated Blood Pressure Measurement in Children","volume":"69","author":"Stergiou","year":"2017","journal-title":"Hypertension"},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"e52794","DOI":"10.2196\/52794","article-title":"Machine Learning-Based Prediction for Incident Hypertension Based on Regular Health Checkup Data: Derivation and Validation in 2 Independent Nationwide Cohorts in South Korea and Japan","volume":"26","author":"Hwang","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_239","doi-asserted-by":"crossref","unstructured":"Islam, S.M.S., Talukder, A., Awal, M.A., Siddiqui, M.M.U., Ahamad, M.M., Ahammed, B., Rawal, L.B., Alizadehsani, R., Abawajy, J., and Laranjo, L. (2022). Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data from Three South Asian Countries. Front. Cardiovasc. Med., 9.","DOI":"10.3389\/fcvm.2022.839379"},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1038\/s41746-025-01491-8","article-title":"A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogram","volume":"8","author":"Friedman","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_241","doi-asserted-by":"crossref","unstructured":"Skalidis, I., Maurizi, N., Salihu, A., Fournier, S., Cook, S., Iglesias, J.F., Laforgia, P., D\u2019Angelo, L., Garot, P., and Hovasse, T. (2025). Artificial Intelligence and Advanced Digital Health for Hypertension: Evolving Tools for Precision Cardiovascular Care. Medicina, 61.","DOI":"10.3390\/medicina61091597"},{"key":"ref_242","first-page":"e83132","article-title":"Artificial Intelligence Tools That Improve Medication Adherence in Patients with Chronic Noncommunicable Diseases: An Updated Review","volume":"17","author":"Bogantes","year":"2025","journal-title":"Cureus"},{"key":"ref_243","doi-asserted-by":"crossref","unstructured":"Reis, Z.S.N., Pereira, G.M.V., Dias, C.d.S., Lage, E.M., de Oliveira, I.J.R., and Pagano, A.S. (2025). Artificial Intelligence-Based Tools for Patient Support to Enhance Medication Adherence: A Focused Review. Front. Digit. Health, 7.","DOI":"10.3389\/fdgth.2025.1523070"},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"e40789","DOI":"10.2196\/40789","article-title":"Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review","volume":"25","author":"Aggarwal","year":"2023","journal-title":"Med. Internet Res."},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1161\/STROKEAHA.116.016281","article-title":"Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy","volume":"48","author":"Labovitz","year":"2017","journal-title":"Stroke"},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"e27466","DOI":"10.2196\/27466","article-title":"Enabling Wearable Pulse Transit Time-Based Blood Pressure Estimation for Medically Underserved Areas and Health Equity: Comprehensive Evaluation Study","volume":"9","author":"Ganti","year":"2021","journal-title":"JMIR mHealth uHealth"},{"key":"ref_247","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1111\/jch.13799","article-title":"The First Study Comparing a Wearable Watch-type Blood Pressure Monitor with a Conventional Ambulatory Blood Pressure Monitor on In-office and Out-of-office Settings","volume":"22","author":"Kario","year":"2020","journal-title":"J. Clin. Hypertens."},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"e30051","DOI":"10.2196\/30051","article-title":"Remote Blood Pressure Monitoring with a Wearable Photoplethysmographic Device (Senbiosys): Protocol for a Single-Center Prospective Clinical Trial","volume":"10","author":"Schukraft","year":"2021","journal-title":"JMIR Res. Protoc."},{"key":"ref_249","doi-asserted-by":"crossref","first-page":"12","DOI":"10.5603\/ah.94874","article-title":"Photoplethysmography Technology Use in Smart Devices for Early Diagnosis of Arterial Hypertension: A Systematic Review","volume":"28","author":"Savickas","year":"2024","journal-title":"Arter. Hypertens."},{"key":"ref_250","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1159\/000522660","article-title":"Challenges Presented by Cuffless Measurement of Blood Pressure if Adopted for Diagnosis and Treatment of Hypertension","volume":"10","author":"Avolio","year":"2022","journal-title":"Pulse"},{"key":"ref_251","doi-asserted-by":"crossref","first-page":"e071952","DOI":"10.1136\/bmj-2022-071952","article-title":"Prevalence, Awareness, Treatment, and Control of Hypertension in China, 2004\u20132018: Findings from Six Rounds of a National Survey","volume":"380","author":"Zhang","year":"2023","journal-title":"BMJ"},{"key":"ref_252","doi-asserted-by":"crossref","first-page":"83","DOI":"10.2478\/jce-2023-0019","article-title":"Applications of Artificial Intelligence in Cardiovascular Emergencies\u2014Status Quo and Outlook","volume":"9","author":"Hatfaludi","year":"2023","journal-title":"J. Cardiovasc. Emergencies"},{"key":"ref_253","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.1097\/HJH.0000000000003205","article-title":"Virtual Management of Hypertension: Lessons from the COVID-19 Pandemic\u2013International Society of Hypertension Position Paper Endorsed by the World Hypertension League and European Society of Hypertension","volume":"40","author":"Khan","year":"2022","journal-title":"J. Hypertens."},{"key":"ref_254","doi-asserted-by":"crossref","unstructured":"Lobo, E.H., Karmakar, C., Abdelrazek, M., Abawajy, J., Chow, C.K., Zhang, Y., Kabir, M.A., Daryabeygi, R., Maddison, R., and Islam, S.M.S. (2023). Design and development of a smartphone app for hypertension management: An intervention mapping approach. Front Public Health, 11.","DOI":"10.3389\/fpubh.2023.1092755"},{"key":"ref_255","doi-asserted-by":"crossref","first-page":"e66804","DOI":"10.2196\/66804","article-title":"Analysis of Factors Influencing the Willingness of Chinese Older Adults to Use mHealth Devices: Nationwide Cross-Sectional Survey Study","volume":"27","author":"Yan","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_256","doi-asserted-by":"crossref","first-page":"e43310","DOI":"10.1097\/MD.0000000000043310","article-title":"Exploring the Emerging Trends and Hot Topics of 5G Technology Application in Wireless Medicine: A Bibliometric and Visualization Analysis","volume":"104","author":"He","year":"2025","journal-title":"Medicine"},{"key":"ref_257","doi-asserted-by":"crossref","unstructured":"Ma, G., Zhong, Z., Duan, Y., Shen, Z., Qin, N., and Hu, D. (2022). Development and Validation of a Self-Quantification Scale for Patients with Hypertension. Front. Public. Health, 10.","DOI":"10.3389\/fpubh.2022.849859"},{"key":"ref_258","first-page":"237","article-title":"Risk Analysis and Countermeasures for Wearable Mobile Medical Devices","volume":"6","author":"Wang","year":"2023","journal-title":"Adv. Econ. Manag. Political Sci."},{"key":"ref_259","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1111\/jch.13713","article-title":"Asian Management of Hypertension: Current Status, Home Blood Pressure, and Specific Concerns in Japan","volume":"22","author":"Kabutoya","year":"2019","journal-title":"J. Clin. Hypertens."},{"key":"ref_260","doi-asserted-by":"crossref","unstructured":"Tapos, G.F., Cimpeanu, I.A., Predescu, I.A., Liga, S., Pacurar, A.T., Vlad, D., Boru, C., Luca, S., Crisan, S., and Vacarescu, C. (2026). An Integrative Review of the Cardiovascular Disease Spectrum: Integrating Multi-Omics and Artificial Intelligence for Precision Cardiology. Diseases, 14.","DOI":"10.3390\/diseases14010031"},{"key":"ref_261","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1038\/s41440-022-00997-y","article-title":"Digital Hypertension 2023: Concept, Hypothesis, and New Technology","volume":"45","author":"Kario","year":"2022","journal-title":"Hypertens. Res."},{"key":"ref_262","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1177\/01410768221077381","article-title":"Use of Lifestyle Interventions in Primary Care for Individuals with Newly Diagnosed Hypertension, Hyperlipidaemia or Obesity: A Retrospective Cohort Study","volume":"115","author":"Lemp","year":"2022","journal-title":"J. R. Soc. Med."},{"key":"ref_263","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.acvd.2025.06.078","article-title":"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","volume":"119","author":"Trimaille","year":"2026","journal-title":"Arch. Cardiovasc. Dis."},{"key":"ref_264","doi-asserted-by":"crossref","first-page":"e67216","DOI":"10.2196\/67216","article-title":"Effectiveness of an mHealth Program on Reducing Blood Pressure Among Young Adults with Prehypertension: Protocol of a Pragmatic Cluster Randomized Controlled Trial","volume":"14","author":"Sheilini","year":"2025","journal-title":"JMIR Res. Protoc."},{"key":"ref_265","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1111\/jch.14183","article-title":"Factors Associated with Hypertension Among Stroke-free Indigenous Africans: Findings from the SIREN Study","volume":"23","author":"Akpa","year":"2021","journal-title":"J. Clin. Hypertens."},{"key":"ref_266","first-page":"1518","article-title":"Assessment of Modifiable Risk Factors Associated with Hypertensive Patients","volume":"4","author":"Awan","year":"2024","journal-title":"J. Health Rehabil. Res."},{"key":"ref_267","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1186\/s40814-021-00925-w","article-title":"Feasibility study to assess the delivery of a novel isometric exercise intervention for people with stage 1 hypertension in the NHS: Protocol for the IsoFIT-BP study including amendments to mitigate the risk of COVID-19","volume":"7","author":"Wiles","year":"2021","journal-title":"Pilot Feasibility Stud."},{"key":"ref_268","doi-asserted-by":"crossref","unstructured":"Walburg, F.S., Meijel, B.v., Tulder, M.W.v., and Adriaanse, M.C. (2019). Cost-Effectiveness of a Lifestyle Intervention for People with a Serious Mental Illness (SMILE): Design of a Pragmatic Cluster-Randomised Controlled Trial. BMC Psychiatry, 19.","DOI":"10.1186\/s12888-019-2132-5"},{"key":"ref_269","doi-asserted-by":"crossref","first-page":"e3760","DOI":"10.1002\/dmrr.3760","article-title":"Chrono-nutrition for Hypertension","volume":"40","author":"Bohmke","year":"2024","journal-title":"Diabetes\/Metab. Res. Rev."},{"key":"ref_270","doi-asserted-by":"crossref","unstructured":"Saranjam, F., Afrasiabifar, A., Alamdari, A., and Hosseini, N. (2023). Effect of Leventhal\u2019s Self-Regulatory Intervention on the Hypertensive Patients\u2019 Illness Perception and Lifestyle: A Randomized Controlled Trial. BMC Cardiovasc. Disord., 23.","DOI":"10.1186\/s12872-023-03049-6"},{"key":"ref_271","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1177\/15598276241242013","article-title":"Impact of Lifestyle Medicine Interventions on the Management of Systemic Hypertension in Primary Care: A Canadian Randomized Controlled Trial","volume":"18","author":"Moulin","year":"2024","journal-title":"Am. J. Lifestyle Med."},{"key":"ref_272","doi-asserted-by":"crossref","first-page":"5586403","DOI":"10.1155\/2023\/5586403","article-title":"Comparative Analysis of Hypertension Guidelines: Unveiling Consensus and Discrepancies in Lifestyle Modifications for Blood Pressure Control","volume":"2023","author":"Wang","year":"2023","journal-title":"Cardiol. Res. Pract."},{"key":"ref_273","doi-asserted-by":"crossref","first-page":"684","DOI":"10.4103\/ijcm.ijcm_780_22","article-title":"A Community Trial in Coastal Karnataka Using Life Style Modifications to Assess Its Impact on Hypertension and Diabetes","volume":"48","author":"Kundapur","year":"2023","journal-title":"Indian J. Commun. Med."},{"key":"ref_274","doi-asserted-by":"crossref","first-page":"6059560","DOI":"10.1155\/2018\/6059560","article-title":"Effect of a Lifestyle-Based Intervention on Health-Related Quality of Life in Older Adults with Hypertension","volume":"2018","author":"Chen","year":"2018","journal-title":"J. Aging Res."},{"key":"ref_275","doi-asserted-by":"crossref","first-page":"e22465","DOI":"10.2196\/22465","article-title":"Web-Based Dietary and Physical Activity Intervention Programs for Patients with Hypertension: Scoping Review","volume":"23","author":"Allam","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_276","doi-asserted-by":"crossref","first-page":"e21759","DOI":"10.2196\/21759","article-title":"The Effect of Smartphone App\u2013Based Interventions for Patients with Hypertension: Systematic Review and Meta-Analysis","volume":"8","author":"Xu","year":"2020","journal-title":"JMIR mHealth uHealth"},{"key":"ref_277","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1111\/aor.13927","article-title":"Role of Implantable Cardioverter-defibrillator in Patients Awaiting Heart Transplant in the Continuous-flow Left Ventricular Assist Device Era","volume":"45","author":"Trivedi","year":"2021","journal-title":"Artif. Organs"},{"key":"ref_278","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1536\/ihj.17-096","article-title":"Utility of Fluid Assessment Based on the Intrathoracic Impedance Monitoring in a Peripartum Woman with Heart Disease","volume":"59","author":"Daimon","year":"2018","journal-title":"Int. Heart J."},{"key":"ref_279","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1111\/pace.14002","article-title":"Do ICD Diagnostics Predict Failure of Ventricular Tachycardia Response to Antitachycardia Pacing and Need for Shock?","volume":"43","author":"Singhvi","year":"2020","journal-title":"Pacing Clin. Electrophysiol."},{"key":"ref_280","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1002\/ejhf.3239","article-title":"New York Heart Association Functional Class and Implantable Cardioverter-defibrillator in Non-ischaemic Heart Failure with Reduced Ejection Fraction: Extended Follow-up of the DANISH Trial","volume":"26","author":"Karacan","year":"2024","journal-title":"Eur. J. Heart Fail."},{"key":"ref_281","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1111\/pace.13163","article-title":"Who Still Remains at Risk of Arrhythmic Death at Time of Implantable Cardioverter-defibrillator Generator Replacement?","volume":"40","author":"Madeira","year":"2017","journal-title":"Pacing Clin. Electrophysiol."},{"key":"ref_282","doi-asserted-by":"crossref","first-page":"e418","DOI":"10.1097\/MD.0000000000000418","article-title":"Efficacy of Implantable Cardioconverter Defibrillator or Cardiac Resynchronization Therapy Compared with Combined Therapy in Survival of Patients with Heart Failure","volume":"94","author":"Deng","year":"2015","journal-title":"Medicine"},{"key":"ref_283","doi-asserted-by":"crossref","first-page":"e002324","DOI":"10.1161\/CIRCHEARTFAILURE.115.002324","article-title":"Comparative Effectiveness of Cardiac Resynchronization Therapy Among Patients with Heart Failure and Atrial Fibrillation","volume":"9","author":"Khazanie","year":"2016","journal-title":"Circ. Heart Fail."},{"key":"ref_284","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.cjco.2019.04.003","article-title":"The Effect of Shock Burden on Heart Failure and Mortality","volume":"1","author":"MacIntyre","year":"2019","journal-title":"CJC Open"},{"key":"ref_285","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1016\/j.jacc.2015.12.046","article-title":"Use of Guideline-Directed Medications for Heart Failure Before Cardioverter-Defibrillator Implantation","volume":"67","author":"Roth","year":"2016","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_286","doi-asserted-by":"crossref","first-page":"e010985","DOI":"10.1136\/heartasia-2017-010985","article-title":"Gender Differences in the Use of Primary Prevention ICDs in New Zealand Patients with Heart Failure","volume":"10","author":"Looi","year":"2018","journal-title":"Heart Asia"},{"key":"ref_287","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1515\/cdbme-2018-1131","article-title":"Pacing Electrode Modeling and Simulation of Cardiac Contractility Modulation","volume":"4","author":"Roller","year":"2018","journal-title":"Curr. Dir. Biomed. Eng."},{"key":"ref_288","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1002\/ehf2.13901","article-title":"Primary Results from the Japanese Heart Failure and Sudden Cardiac Death Prevention Trial (HINODE)","volume":"9","author":"Aonuma","year":"2022","journal-title":"Esc. Heart Fail."},{"key":"ref_289","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1001\/jamainternmed.2017.8462","article-title":"Long-Term Outcomes Associated with Implantable Cardioverter Defibrillator in Adults with Chronic Kidney Disease","volume":"178","author":"Bansal","year":"2018","journal-title":"JAMA Intern. Med."},{"key":"ref_290","doi-asserted-by":"crossref","first-page":"h3529","DOI":"10.1136\/bmj.h3529","article-title":"Real World Effectiveness of Primary Implantable Cardioverter Defibrillators Implanted During Hospital Admissions for Exacerbation of Heart Failure or Other Acute Co-Morbidities: Cohort Study of Older Patients with Heart Failure","volume":"351","author":"Chen","year":"2015","journal-title":"BMJ"},{"key":"ref_291","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1002\/joa3.12027","article-title":"Long-term Outcomes of Heart Failure Patients Who Received Primary Prevention Implantable Cardioverter-defibrillator: An Observational Study","volume":"34","author":"Looi","year":"2017","journal-title":"J. Arrhythmia"},{"key":"ref_292","first-page":"417","article-title":"Effectiveness of Implantation of Cardioverter-Defibrillators Therapy in Patients with Non-Ischemic Heart Failure: An Updated Systematic Review and Meta-Analysis","volume":"32","author":"Xing","year":"2017","journal-title":"Braz. J. Cardiovasc. Surg."},{"key":"ref_293","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1002\/joa3.12244","article-title":"Implantable Cardioverter Defibrillator and Cardiac Resynchronization Therapy Use in New Zealand (ANZACS-QI 33)","volume":"36","author":"Foo","year":"2019","journal-title":"J. Arrhythmia"},{"key":"ref_294","doi-asserted-by":"crossref","first-page":"1146235","DOI":"10.3346\/jkms.2020.35.e397","article-title":"Implantable Cardioverter-Defibrillator Utilization and Its Outcomes in Korea: Data from Korean Acute Heart Failure Registry","volume":"35","author":"Cho","year":"2020","journal-title":"J. Korean Med. Sci."},{"key":"ref_295","doi-asserted-by":"crossref","first-page":"3270","DOI":"10.1038\/s41591-025-03983-2","article-title":"Generative artificial intelligence in medicine","volume":"31","author":"Teo","year":"2025","journal-title":"Nat. Med."},{"key":"ref_296","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1161\/CIRCULATIONAHA.115.021048","article-title":"Sex and Race\/Ethnicity Differences in Implantable Cardioverter-Defibrillator Counseling and Use Among Patients Hospitalized with Heart Failure","volume":"134","author":"Hess","year":"2016","journal-title":"Circulation"},{"key":"ref_297","doi-asserted-by":"crossref","unstructured":"Ursaru, A.M., Petri\u0219, A.O., Costache, I.I., and Tesloianu, D. (2021). Comparable Efficacy in Ischemic and Non-Ischemic ICD Recipients for the Primary Prevention of Sudden Cardiac Death. Biomedicines, 9.","DOI":"10.20944\/preprints202108.0565.v1"},{"key":"ref_298","doi-asserted-by":"crossref","first-page":"e005820","DOI":"10.1161\/CIRCEP.117.005820","article-title":"Clinical Course and Quality of Life in High-Risk Patients with Hypertrophic Cardiomyopathy and Implantable Cardioverter-Defibrillators","volume":"11","author":"Maron","year":"2018","journal-title":"Circ. Arrhythmia Electrophysiol."},{"key":"ref_299","doi-asserted-by":"crossref","unstructured":"Cui, F., Tong, Y., Liu, G., Zhang, W., Liu, K., Si, D., and He, Y. (2023). Intracoronary Imaging-Guided Rotational Atherectomy Combined with Intravascular Lithotripsy in the Treatment of Severe Coronary Artery Calcification\u2014A Case Report. Front. Cardiovasc. Med., 10.","DOI":"10.3389\/fcvm.2023.1184237"},{"key":"ref_300","first-page":"809","article-title":"Comparison of Long-Term Outcomes Between Intravascular Ultrasound-, Optical Coherence Tomography- and Angiography-Guided Stent Implantation: A Meta-Analysis","volume":"75","author":"Akbulut","year":"2023","journal-title":"Angiology"},{"key":"ref_301","first-page":"202","article-title":"National Trends, Mortality and Outcomes in Intravascular Imaging-Guided Versus Angiography-Guided Percutaneous Coronary Intervention in the United States","volume":"23","author":"Krittanawong","year":"2024","journal-title":"Crit. Pathw. Cardiol. A J. Evid.-Based Med."},{"key":"ref_302","doi-asserted-by":"crossref","first-page":"334","DOI":"10.11604\/pamj.2020.36.334.25546","article-title":"Conservative Management of Spontaneous Coronary Artery Dissection: A Case Report","volume":"36","author":"Regragui","year":"2020","journal-title":"Pan Afr. Med. J."},{"key":"ref_303","doi-asserted-by":"crossref","first-page":"969","DOI":"10.33963\/KP.a2023.0151","article-title":"Factors Determining the Frequency of Optical Coherence Tomography and Intravascular Ultrasound Use in Patients Treated with Percutaneous Coronary Interventions in Recent Years: Analysis Based on a Large National Registry","volume":"81","author":"Januszek","year":"2023","journal-title":"Kardiol. Pol."},{"key":"ref_304","first-page":"195","article-title":"Assessing Diagnostic Accuracy of Intravascular Imaging for Functionally Significant Coronary Stenosis: Updated Findings from a Meta-Analysis","volume":"111","author":"Improta","year":"2024","journal-title":"Heart"},{"key":"ref_305","doi-asserted-by":"crossref","unstructured":"Krupnikovi\u0107, K., Obradovi\u0107, D., Ili\u0107, I., and Dobri\u0107, M. (2025). Multimodality Imaging and Advanced Calcium Treatment to Facilitate PCI in a Rare Coronary Artery Anomaly\u2014Case Report. Front. Cardiovasc. Med., 12.","DOI":"10.3389\/fcvm.2025.1471211"},{"key":"ref_306","first-page":"E1","article-title":"Outcomes of Intravascular Ultrasound Versus Optical Coherence Tomography Guided Percutaneous Coronary Angiography: A Meta regression-basedAnalysis","volume":"99","author":"Sattar","year":"2021","journal-title":"Catheter. Cardiovasc. Interv."},{"key":"ref_307","first-page":"33","article-title":"Role of Optical Coherence Tomography in Coronary Interventions","volume":"5","author":"Maddali","year":"2024","journal-title":"Indian. J. Clin. Cardiol."},{"key":"ref_308","doi-asserted-by":"crossref","unstructured":"Ali, W.B., Pesaranghader, A., Avram, R., Overtchouk, P., Perrin, N., Laffite, S., Cartier, R., Ibrahim, R., Modine, T., and Hussin, J. (2021). Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble. Front. Cardiovasc. Med., 8.","DOI":"10.3389\/fcvm.2021.711401"},{"key":"ref_309","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s40119-020-00185-4","article-title":"Optical Coherence Tomography: Current Applications for the Assessment of Coronary Artery Disease and Guidance of Percutaneous Coronary Interventions","volume":"9","author":"Oosterveer","year":"2020","journal-title":"Cardiol. Ther."},{"key":"ref_310","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1536\/ihj.18-448","article-title":"Impact of High-Sensitivity Cardiac Troponin Elevation in Relation to Diagnostic Invasive Intravascular Imaging for the Assessment of Coronary Artery Disease","volume":"60","author":"Sanuki","year":"2019","journal-title":"Int. Heart J."},{"key":"ref_311","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1097\/CM9.0000000000002117","article-title":"Wearable Sensing, Big Data Technology for Cardiovascular Healthcare: Current Status and Future Prospective","volume":"136","author":"Miao","year":"2022","journal-title":"Chin. Med. J."},{"key":"ref_312","doi-asserted-by":"crossref","unstructured":"Nekui, O.D., Wang, W., Liu, C., Wang, Z., and Ding, B. (2024). IoT-Based Heartbeat Rate-Monitoring Device Powered by Harvested Kinetic Energy. Sensors, 24.","DOI":"10.3390\/s24134249"},{"key":"ref_313","doi-asserted-by":"crossref","first-page":"e74071","DOI":"10.2196\/74071","article-title":"Wearable Devices for Remote Monitoring of Chronic Diseases: Systematic Review","volume":"14","author":"Tegegne","year":"2026","journal-title":"JMIR Mhealth Uhealth."},{"key":"ref_314","doi-asserted-by":"crossref","first-page":"151","DOI":"10.4236\/jbise.2019.122011","article-title":"Wearable Wireless Body Area Nodes for Remote Physiological Signal Monitoring System","volume":"12","author":"Muralidhara","year":"2019","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref_315","doi-asserted-by":"crossref","first-page":"1602790","DOI":"10.1002\/smll.201602790","article-title":"Flexible Sensing Electronics for Wearable\/Attachable Health Monitoring","volume":"13","author":"Wang","year":"2017","journal-title":"Small"},{"key":"ref_316","doi-asserted-by":"crossref","unstructured":"Dias, D., and Paulo Silva Cunha, J. (2018). Wearable Health Devices-Vital Sign Monitoring, Systems and Technologies. Sensors, 18.","DOI":"10.3390\/s18082414"},{"key":"ref_317","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1038\/s41467-021-21271-8","article-title":"A Novel Art of Continuous Noninvasive Blood Pressure Measurement","volume":"12","author":"Fortin","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_318","doi-asserted-by":"crossref","first-page":"18","DOI":"10.63345\/ijrmeet.org.v10.i12.3","article-title":"Advancements in Flexible Electronics for Wearable Health Monitoring Devices","volume":"10","author":"Mishra","year":"2022","journal-title":"IJRMEET"},{"key":"ref_319","doi-asserted-by":"crossref","unstructured":"Weaver, K., Shumway, D., Yang, T.H., Kim, Y.-M., and Koo, J.H. (2019, January 9\u201311). Investigation of Variable Stiffness Effects on Radial Pulse Measurements Using Magneto-Rheological Elastomers. Proceedings of the ASME 2019 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Louisville, KY, USA.","DOI":"10.1115\/SMASIS2019-5708"},{"key":"ref_320","doi-asserted-by":"crossref","first-page":"10837","DOI":"10.3390\/s101210837","article-title":"Detecting Vital Signs with Wearable Wireless Sensors","volume":"10","author":"Yilmaz","year":"2010","journal-title":"Sensors"},{"key":"ref_321","doi-asserted-by":"crossref","unstructured":"Abdellatif, S.O., Moustafa, A.G., Ahmed, K., and Ghannam, R. (2023). Integration of Capacitive Pressure Sensor-on-Chip with Lead-Free Perovskite Solar Cells for Continuous Health Monitoring. Micromachines, 14.","DOI":"10.3390\/mi14091676"},{"key":"ref_322","doi-asserted-by":"crossref","unstructured":"An, B.W., Shin, J.H., Kim, S.Y., Kim, J., Ji, S., Park, J., Lee, Y., Jang, J., Park, Y.G., and Cho, E. (2017). Smart Sensor Systems for Wearable Electronic Devices. Polymers, 9.","DOI":"10.3390\/polym9080303"},{"key":"ref_323","doi-asserted-by":"crossref","first-page":"39025","DOI":"10.31083\/RCM39025","article-title":"Wearable Technology in Cardiology: Advancements, Applications, and Future Prospects","volume":"26","author":"Jena","year":"2025","journal-title":"Rev. Cardiovasc. Med."},{"key":"ref_324","doi-asserted-by":"crossref","unstructured":"Leszczy\u0144ska, A. (2025). The Integration of Artificial Intelligence Into Robotic Cancer Surgery: A Systematic Review. J. Clin. Med., 14.","DOI":"10.3390\/jcm14176181"},{"key":"ref_325","doi-asserted-by":"crossref","first-page":"391","DOI":"10.5606\/tgkdc.dergisi.2021.21326","article-title":"Robotic Redo Mitral Valve Replacement Combined with Dysfunctional Amplatzer Plug Resection on a Beating Heart","volume":"29","author":"Bolcal","year":"2021","journal-title":"Turk. J. Thorac. Cardiovasc. Surg."},{"key":"ref_326","doi-asserted-by":"crossref","first-page":"e2304","DOI":"10.1002\/rcs.2304","article-title":"Robot-assisted Beating-heart Surgery for Atrial Septal Defect Repair in a Case of Situs Inversus Totalis with Dextrocardia","volume":"17","author":"Li","year":"2021","journal-title":"Int. J. Med. Robot. Comput. Assist. Surg."},{"key":"ref_327","doi-asserted-by":"crossref","unstructured":"Shiferaw, K.B., Wali, P., Waltemath, D., and Zeleke, A.A. (2024). Navigating the AI Frontiers in Cardiovascular Research: A Bibliometric Exploration and Topic Modeling. Front. Cardiovasc. Med., 10.","DOI":"10.3389\/fcvm.2023.1308668"},{"key":"ref_328","first-page":"1","article-title":"Perceptions of Cardiac Surgeons Regarding the Integration of Artificial Intelligence in Cardiac Surgery","volume":"37","author":"Alguizzani","year":"2025","journal-title":"J. Saudi Heart Assoc."},{"key":"ref_329","doi-asserted-by":"crossref","unstructured":"Leivaditis, V., Beltsios, E., Papatriantafyllou, A., Grapatsas, K., Mulita, F., Kontodimopoulos, N., Baikoussis, N.G., Tchabashvili, L., Tasios, K., and Maroulis, I. (2025). Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future. Clin. Pract., 15.","DOI":"10.3390\/clinpract15010017"},{"key":"ref_330","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1177\/1556984520922978","article-title":"Adult Ventricular Septal Defect Repair Using a Robotic Totally Endoscopic Approach: A Case Report","volume":"15","author":"Mirzai","year":"2020","journal-title":"Innov. Technol. Tech. Cardiothorac. Vasc. Surg."},{"key":"ref_331","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1111\/jocs.14291","article-title":"Robotic Totally Endoscopic Excision of Aortic Valve Papillary Fibroelastoma: The Least Invasive Approach","volume":"34","author":"Nisivaco","year":"2019","journal-title":"J. Card. Surg."},{"key":"ref_332","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1111\/jocs.14128","article-title":"Redo Robotic Cardiac Surgery and Concomitant Cesarean Section in a Pregnant Patient with Dextrocardia and Situs Inversus Totalis","volume":"34","author":"Bolcal","year":"2019","journal-title":"J. Card. Surg."},{"key":"ref_333","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1186\/s13019-022-01978-5","article-title":"Robot-Assisted Endoscopic Removal of a Huge Tricuspid Valve Myxoma: Case Report","volume":"17","author":"Hung","year":"2022","journal-title":"J. Cardiothorac. Surg."},{"key":"ref_334","first-page":"1","article-title":"Left Internal Mammary Artery to Left Anterior Descending Artery. An Alternative for Old, High-Risk, Multivessel, Coronary Artery Disease Patients and an Approach for Hybrid Revascularization","volume":"5","author":"Benetti","year":"2022","journal-title":"Clin. Cardiol. Cardiovasc. Interv."},{"key":"ref_335","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1111\/jocs.15281","article-title":"An Analysis of The learning Curve for Robotic-assisted Mitral Valve Repair","volume":"36","author":"Zencirci","year":"2021","journal-title":"J. Card. Surg."},{"key":"ref_336","doi-asserted-by":"crossref","unstructured":"Raharjo, S.W., Suwadi, P., and Novianto, W.T. (2024). An Ideal Regulatory Framework for Robotic Surgery Utilization in Indonesia. Proceedings of the International Conference on Law, Economic & Good Governance (IC-LAW 2023), Atlantis Press.","DOI":"10.2991\/978-2-38476-218-7_106"},{"key":"ref_337","doi-asserted-by":"crossref","first-page":"e2395","DOI":"10.1002\/rcs.2395","article-title":"Robotic Repair of Atrial Septal Defect with Partial Pulmonary Venous Return Anomaly: Our 5 year Experience","volume":"18","author":"Kadan","year":"2022","journal-title":"Int. J. Med. Robot. Comput. Assist. Surg."},{"key":"ref_338","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1177\/15569845231194613","article-title":"Simple Tool for Bleeding Control During Minimally Invasive and Robotic Cardiac Surgery","volume":"18","author":"Arslanhan","year":"2023","journal-title":"Innov. Technol. Tech. Cardiothorac. Vasc. Surg."},{"key":"ref_339","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1111\/aor.13896","article-title":"Combined Robot-assisted Mitral Valve Plasty and Nuss Procedure via Small Ports","volume":"45","author":"Ishikawa","year":"2021","journal-title":"Artif. Organs"},{"key":"ref_340","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13017-022-00413-3","article-title":"Knowledge, Attitude, and Practice of Artificial Intelligence in Emergency and Trauma Surgery, the ARIES Project: An International Web-Based Survey","volume":"17","author":"Simone","year":"2022","journal-title":"World J. Emerg. Surg."},{"key":"ref_341","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Xu, W., Zeng, Y., Li, H., Liu, Z., Wang, T., Liu, J., and Jiang, H. (2023). Global Research Trends of the Application of Artificial Intelligence in Bladder Cancer Since the 21st Century: A Bibliometric Analysis. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1227152"},{"key":"ref_342","first-page":"165","article-title":"Robotic-assisted Cardiac Surgery without Aortic Cross-clamping: A Safe Alternative Approach","volume":"36","author":"Ersin","year":"2020","journal-title":"J. Card. Surg."},{"key":"ref_343","doi-asserted-by":"crossref","unstructured":"Qiu, Y.-H., Li, Y., Chen, Z., Chai, N.-H., Liang, X., Zhang, D., and Wei, Z.Q. (2023). Application of the Advance Incision in Robotic-Assisted Laparoscopic Rectal Anterior Resection. Front. Surg., 10.","DOI":"10.3389\/fsurg.2023.1141672"},{"key":"ref_344","doi-asserted-by":"crossref","first-page":"74","DOI":"10.69580\/IJPPR.15.2.2024.74-81","article-title":"Pharmacotherapy Strategies for Metabolic and Endocrine Disorder","volume":"15","author":"Mishra","year":"2024","journal-title":"IJPPR"},{"key":"ref_345","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3897\/rrpharmacology.6.54130","article-title":"Medication Adherence in Patients with Stable Coronary Artery Disease in Primary Care","volume":"6","author":"Zyryanov","year":"2020","journal-title":"Res. Results Pharmacol."},{"key":"ref_346","doi-asserted-by":"crossref","first-page":"4211","DOI":"10.1097\/JS9.0000000000000705","article-title":"Revolutionizing clinical trials: The role of AI in accelerating medical breakthroughs","volume":"109","author":"Chopra","year":"2023","journal-title":"Int. J. Surg."},{"key":"ref_347","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1097\/ADM.0000000000000509","article-title":"Medication Adherence Monitoring Using Smartphone Video Dosing in an Open-Label Pilot Study of Monthly Naltrexone Plus Once-Daily Bupropion for Methamphetamine Use Disorder: Feasibility and Acceptability","volume":"13","author":"Walker","year":"2019","journal-title":"J. Addict. Med."},{"key":"ref_348","doi-asserted-by":"crossref","unstructured":"Straka, I., Min\u00e1r, M., \u0160korv\u00e1nek, M., Grofik, M., Danterov\u00e1, K., Benetin, J., Kur\u010da, E., Ga\u017eov\u00e1, A., Bolekov\u00e1, V., and Wyman-Chick, K.A. (2019). Adherence to Pharmacotherapy in Patients with Parkinson\u2019s Disease Taking Three and More Daily Doses of Medication. Front. Neurol., 10.","DOI":"10.3389\/fneur.2019.00799"},{"key":"ref_349","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1097\/FJC.0000000000001508","article-title":"Efficacy and Safety of a Polypill to Reduce Cardiovascular Events: A Review of Clinical Trials","volume":"83","author":"Kelly","year":"2024","journal-title":"J. Cardiovasc. Pharmacol."},{"key":"ref_350","doi-asserted-by":"crossref","unstructured":"Alalaqi, A., Lawson, G., Obaid, Y., and Tanna, S. (2021). Adherence to Cardiovascular Pharmacotherapy by Patients in Iraq: A Mixed Methods Assessment Using Quantitative Dried Blood Spot Analysis and the 8-Item Morisky Medication Adherence Scale. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251115"},{"key":"ref_351","doi-asserted-by":"crossref","first-page":"338","DOI":"10.5530\/jyp.2023.15.45","article-title":"Medication Regimen Complexity Index After Kidney Transplant in Fortaleza, Brazil","volume":"15","author":"Martins","year":"2023","journal-title":"J. Young Pharm."},{"key":"ref_352","doi-asserted-by":"crossref","first-page":"e42474","DOI":"10.2196\/42474","article-title":"The Use of eHealth for Pharmacotherapy Management with Patients with Respiratory Disease, Cardiovascular Disease, or Diabetes: Scoping Review","volume":"25","author":"Bakema","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_353","doi-asserted-by":"crossref","first-page":"100144","DOI":"10.1016\/j.ajpc.2020.100144","article-title":"Real-World Utilization of Pharmacotherapy with New Evidence-Based Cardiovascular Indications in an Academic Preventive Cardiology Practice","volume":"5","author":"Warden","year":"2021","journal-title":"Am. J. Prev. Cardiol."},{"key":"ref_354","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1111\/pcn.12809","article-title":"Non-adherence to Psychotropic Medication Assessed by Plasma Level in Newly Admitted Psychiatric Patients: Prevalence Before Acute Admission","volume":"73","author":"Geretsegger","year":"2019","journal-title":"Psychiatry Clin. Neurosci."},{"key":"ref_355","doi-asserted-by":"crossref","unstructured":"Mohsen, F., Al-Saadi, B., Abdi, N., Khan, S., and Shah, Z. (2023). Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine. J. Pers. Med., 13.","DOI":"10.3390\/jpm13081268"},{"key":"ref_356","doi-asserted-by":"crossref","unstructured":"Wang, J., Tang, X., Lan, P., Liang, B., Fang, Y., and Li, H. (2025). Potential Therapeutic Mechanisms of Draconis Resina in Cardiovascular Diseases-a Narrative Review. Front. Pharmacol., 16.","DOI":"10.3389\/fphar.2025.1531873"},{"key":"ref_357","doi-asserted-by":"crossref","unstructured":"Yang, W.Y., Mahgoub, M., Saaoud, F., Xu, K., Shao, Y., Lu, Y., Dornas, W., Cueto, R., Jiang, X., and Wang, H. (2024). Perspective: Pathological Transdifferentiation\u2014A Novel Therapeutic Target for Cardiovascular Diseases and Chronic Inflammation. Front. Cardiovasc. Med., 11.","DOI":"10.3389\/fcvm.2024.1500775"},{"key":"ref_358","doi-asserted-by":"crossref","first-page":"26","DOI":"10.53941\/ijddp.0201003","article-title":"Is miR-21 a Therapeutic Target in Cardiovascular Disease?","volume":"2","author":"Holland","year":"2023","journal-title":"Int. J. Drug Discov. Pharmacol."},{"key":"ref_359","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1161\/CIRCULATIONAHA.125.076279","article-title":"Phenotypic Selectivity of Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction","volume":"152","author":"Croon","year":"2025","journal-title":"Circulation"},{"key":"ref_360","doi-asserted-by":"crossref","first-page":"e000097","DOI":"10.1161\/HCG.0000000000000097","article-title":"Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement from the American Heart Association","volume":"18","author":"Khera","year":"2025","journal-title":"Circ. Genom. Precis. Med."},{"key":"ref_361","first-page":"6969740","article-title":"The Neuroprotective Effect of Shenmai Injection on Oxidative Stress Injury in PC12 Cells Based on Network Pharmacology","volume":"2022","author":"Wu","year":"2022","journal-title":"Evid.-Based Complement. Altern. Med."},{"key":"ref_362","doi-asserted-by":"crossref","unstructured":"Battaglini, D., Pimentel-Coelho, P.M., Robba, C., Santos, C.C.d., Cruz, F.F., Pelosi, P., and Rocco, P.R.M. (2020). Gut Microbiota in Acute Ischemic Stroke: From Pathophysiology to Therapeutic Implications. Front. Neurol., 11.","DOI":"10.3389\/fneur.2020.00598"},{"key":"ref_363","doi-asserted-by":"crossref","unstructured":"Petzer, V., Theurl, I., and Wei\u00df, G. (2018). Established and Emerging Concepts to Treat Imbalances of Iron Homeostasis in Inflammatory Diseases. Pharmaceuticals, 11.","DOI":"10.3390\/ph11040135"},{"key":"ref_364","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1002\/j.2040-4603.2021.tb00186.x","article-title":"Application of Artificial Intelligence in Cardiovascular Medicine","volume":"11","author":"Cheng","year":"2021","journal-title":"Compr. Physiol."},{"key":"ref_365","doi-asserted-by":"crossref","unstructured":"Sethi, Y., Patel, N., Kaka, N., Desai, A., Kaiwan, O., Sheth, M., Sharma, R., Huang, H., Chopra, H., and Khandaker, M.U. (2022). Artificial Intelligence in Pediatric Cardiology: A Scoping Review. J. Clin. Med., 11.","DOI":"10.3390\/jcm11237072"},{"key":"ref_366","doi-asserted-by":"crossref","unstructured":"Stamate, E., Piraianu, A.I., Ciobotaru, O.R., Crassas, R., Duca, O.-M., Fulga, A., Grigore, I., Vintila, V., Fulga, I., and Ciobotaru, O.C. (2024). Revolutionizing Cardiology Through Artificial Intelligence\u2014Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment\u2014A Comprehensive Review of the Past 5 Years. Diagnostics, 14.","DOI":"10.3390\/diagnostics14111103"},{"key":"ref_367","doi-asserted-by":"crossref","unstructured":"Rudnicka, Z., Proniewska, K., Perkins, M.D., and Pr\u0119gowska, A. (2024). Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality\u2014A Systematic Review. Electronics, 13.","DOI":"10.3390\/electronics13050866"},{"key":"ref_368","doi-asserted-by":"crossref","unstructured":"Khan, M.R., Haider, Z.M., Hussain, J., Malik, F.H., Talib, I., and Abdullah, S. (2024). Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations. Bioengineering, 11.","DOI":"10.3390\/bioengineering11121239"},{"key":"ref_369","doi-asserted-by":"crossref","first-page":"5885","DOI":"10.1002\/alz.13412","article-title":"Artificial Intelligence for Diagnostic and Prognostic Neuroimaging in Dementia: A Systematic Review","volume":"19","author":"Borchert","year":"2023","journal-title":"Alzheimer S Dement."},{"key":"ref_370","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1136\/thorax-2024-222125","article-title":"Harnessing AI in Critical Care: Opportunities, Challenges and Key Steps for Success","volume":"81","author":"Kalimouttou","year":"2025","journal-title":"Thorax"},{"key":"ref_371","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/s41746-020-0261-3","article-title":"Artificial Intelligence for the Diagnosis of Heart Failure","volume":"3","author":"Choi","year":"2020","journal-title":"npj Digit. Med."},{"key":"ref_372","doi-asserted-by":"crossref","unstructured":"Koulaouzidis, G., Jadczyk, T., Iakovidis, D.K., Koulaouzidis, A., Bisnaire, M., and Charisopoulou, D. (2022). Artificial Intelligence in Cardiology\u2014A Narrative Review of Current Status. J. Clin. Med., 11.","DOI":"10.3390\/jcm11133910"},{"key":"ref_373","doi-asserted-by":"crossref","unstructured":"Pantelidis, P., Dilaveris, P., Ruip\u00e9rez-Campillo, S., Goliopoulou, A., Giannakodimos, A., Theofilis, P., Lucia, R.D., \u039a\u03b1\u03c4\u03c3\u03b1\u03c1\u03bf\u03cd, O., Zisimos, K., and Kalogeras, K. (2025). Hearts, Data, and Artificial Intelligence Wizardry: From Imitation to Innovation in Cardiovascular Care. Biomedicines, 13.","DOI":"10.3390\/biomedicines13051019"},{"key":"ref_374","first-page":"300","article-title":"Is the FDA Regulation of Cardiology AI Devices Supporting Cardiovascular Innovation: A Scoping Review","volume":"6","author":"Hussain","year":"2025","journal-title":"Heart"},{"key":"ref_375","doi-asserted-by":"crossref","first-page":"89132","DOI":"10.51894\/001c.89132","article-title":"Using Artificial Intelligence to Semi-Quantitate Coronary Calcium as an \u2018Incidentaloma\u2019 on Non-Gated, Non-Contrast CT Scans, a Single-Center Descriptive Study in West Michigan","volume":"8","author":"Kerndt","year":"2023","journal-title":"Spartan Med. Res. J."},{"key":"ref_376","first-page":"e74681","article-title":"Artificial Intelligence-Driven Advances in Coronary Calcium Scoring: Expanding Preventive Cardiology","volume":"16","author":"Vivekanandan","year":"2024","journal-title":"Cureus"},{"key":"ref_377","doi-asserted-by":"crossref","first-page":"77","DOI":"10.47144\/phj.v57i2.2798","article-title":"Artificial Intelligence the Future of Cardiology","volume":"57","author":"Ashraf","year":"2024","journal-title":"Pak. Heart J."},{"key":"ref_378","doi-asserted-by":"crossref","first-page":"e25000","DOI":"10.2196\/25000","article-title":"Mortality Prediction of Patients with Cardiovascular Disease Using Medical Claims Data Under Artificial Intelligence Architectures: Validation Study","volume":"9","author":"Tran","year":"2021","journal-title":"JMIR Med. Inform."},{"key":"ref_379","first-page":"e83464","article-title":"The Role of Artificial Intelligence in Providing Real-Time Guidance During Interventional Cardiology Procedures: A Narrative Review","volume":"17","author":"Yannakula","year":"2025","journal-title":"Cureus"},{"key":"ref_380","doi-asserted-by":"crossref","unstructured":"Karatzia, L., Aung, N., and Aksentijevi\u0107, D. (2022). Artificial Intelligence in Cardiology: Hope for the Future and Power for the Present. Front. Cardiovasc. Med., 9.","DOI":"10.3389\/fcvm.2022.945726"},{"key":"ref_381","doi-asserted-by":"crossref","first-page":"e14364","DOI":"10.1111\/eci.14364","article-title":"The Role of Artificial Intelligence in Cardiovascular Research: Fear Less and Live Bolder","volume":"55","author":"Scuricini","year":"2025","journal-title":"Eur. J. Clin. Investig."},{"key":"ref_382","doi-asserted-by":"crossref","first-page":"e26646","DOI":"10.2196\/26646","article-title":"Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey","volume":"23","author":"Fritsch","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_383","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1097\/HCO.0000000000000927","article-title":"Artificial Intelligence in Pediatric Cardiology: Taking Baby Steps in the Big World of Data","volume":"37","author":"Eynde","year":"2021","journal-title":"Curr. Opin. Cardiol."},{"key":"ref_384","doi-asserted-by":"crossref","first-page":"e70087","DOI":"10.1002\/clc.70087","article-title":"Advancements in Artificial Intelligence in Noninvasive Cardiac Imaging: A Comprehensive Review","volume":"48","author":"Ezekwueme","year":"2025","journal-title":"Clin. Cardiol."},{"key":"ref_385","first-page":"e78994","article-title":"Non-Invasive Assessment of Coronary Artery Disease: The Role of AI in the Current Status and Future Directions","volume":"17","author":"Ezekwueme","year":"2025","journal-title":"Cureus"},{"key":"ref_386","doi-asserted-by":"crossref","unstructured":"Leone, D.M., O\u2019Sullivan, D., and Jaimes, K.B. (2024). Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review. Children, 12.","DOI":"10.3390\/children12010025"},{"key":"ref_387","doi-asserted-by":"crossref","unstructured":"Padovani, P., Singh, Y., Pass, R.H., Vasile, C.M., Nield, L.E., and Baruteau, A.E. (2023). E-Health: A Game Changer in Fetal and Neonatal Cardiology?. J. Clin. Med., 12.","DOI":"10.3390\/jcm12216865"},{"key":"ref_388","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1097\/ACM.0000000000004963","article-title":"Competencies for the Use of Artificial Intelligence\u2013Based Tools by Health Care Professionals","volume":"98","author":"Russell","year":"2022","journal-title":"Acad. Med."},{"key":"ref_389","doi-asserted-by":"crossref","unstructured":"Gene\u015f, M., and Deveci, B. (2024). A Clinical Evaluation of Cardiovascular Emergencies: A Comparison of Responses from ChatGPT, Emergency Physicians, and Cardiologists. Diagnostics, 14.","DOI":"10.3390\/diagnostics14232731"},{"key":"ref_390","doi-asserted-by":"crossref","unstructured":"Mahdavi, M., and Frings, D. (2024). Trust in AI Applications and Intention to Use Them in Cardiac Care Among Cardiologists in the UK: A Structural Equation Modeling Approach. preprint.","DOI":"10.21203\/rs.3.rs-4114716\/v1"},{"key":"ref_391","doi-asserted-by":"crossref","first-page":"36","DOI":"10.17987\/icfj.v6i0.187","article-title":"Desirable Characteristics of Consulting Cardiologists for Sustained Referrals by Primary Care Physicians","volume":"6","author":"Ribeiro","year":"2016","journal-title":"Int. Cardiovasc. Forum J."},{"key":"ref_392","doi-asserted-by":"crossref","first-page":"e002909","DOI":"10.1161\/JAHA.115.002909","article-title":"Providing Cardiology Care in Rural Areas Through Visiting Consultant Clinics","volume":"5","author":"Gruca","year":"2016","journal-title":"J. Am. Heart Assoc."},{"key":"ref_393","doi-asserted-by":"crossref","unstructured":"Helm, P., Kaemmerer, H., Breithardt, G., Sticker, E., Keuchen, R., Neidenbach, R., Diller, G.P., Tutarel, O., and Bauer, U. (2017). Transition in Patients with Congenital Heart Disease in Germany: Results of a Nationwide Patient Survey. Front. Pediatr., 5.","DOI":"10.3389\/fped.2017.00115"},{"key":"ref_394","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.cvdhj.2021.04.002","article-title":"An Artificial Intelligence\u2013enabled ECG Algorithm for Comprehensive ECG Interpretation: Can It Pass the \u2018Turing Test\u2019?","volume":"2","author":"Kashou","year":"2021","journal-title":"Cardiovasc. Digit. Health J."},{"key":"ref_395","doi-asserted-by":"crossref","first-page":"14067","DOI":"10.1038\/s41598-022-18473-5","article-title":"Automated Diagnosis of Optical Coherence Tomography Imaging on Plaque Vulnerability and Its Relation to Clinical Outcomes in Coronary Artery Disease","volume":"12","author":"Niioka","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_396","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1108\/LHTN-03-2024-0048","article-title":"Impact of Artificial Intelligence on Health Information Literacy: Guidance for Healthcare Professionals","volume":"41","author":"Adegboye","year":"2024","journal-title":"Libr. Hi Tech. News"},{"key":"ref_397","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1093\/ehjdh\/ztae017","article-title":"Artificial Intelligence-Assisted Evaluation of Cardiac Function by Oncology Staff in Chemotherapy Patients","volume":"5","author":"Papadopoulou","year":"2024","journal-title":"Eur. Heart J.-Digit. Health"},{"key":"ref_398","doi-asserted-by":"crossref","first-page":"17539447251406847","DOI":"10.1177\/17539447251406847","article-title":"Artificial intelligence for cardiology: From diagnosis to management","volume":"20","author":"Naidoo","year":"2026","journal-title":"Ther. Adv. Cardiovasc. Dis."},{"key":"ref_399","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1097\/MS9.0000000000004607","article-title":"Artificial intelligence in cardiology: An updated systematic review with ethical considerations and challenges in implementing artificial intelligence models","volume":"88","author":"Patel","year":"2026","journal-title":"Ann. Med. Surg."},{"key":"ref_400","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1053\/j.semnuclmed.2025.11.011","article-title":"Artificial intelligence in nuclear cardiology: Technical perspectives, strategic directions, and recommendations from an IAEA expert working group","volume":"56","author":"Wiefels","year":"2026","journal-title":"Semin. Nucl. Med."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/16\/4\/192\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T04:19:14Z","timestamp":1775189954000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/16\/4\/192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,1]]},"references-count":400,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["jpm16040192"],"URL":"https:\/\/doi.org\/10.3390\/jpm16040192","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,1]]}}}