{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T23:05:16Z","timestamp":1793833516723,"version":"4.6.1"},"reference-count":115,"publisher":"Oxford University Press (OUP)","issue":"37","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Recent advances in artificial intelligence (AI) have shown great potential in improving cardiovascular pharmacotherapy by optimizing drug selection, predicting therapeutic efficacy and adverse effects, ultimately improving patient outcomes. Leveraging techniques like machine learning and in silico modelling, AI can identify populations likely to benefit from specific treatments, expedite novel drug discovery and reduce costs. Computational methods can also facilitate the detection of drug interactions and tailor interventions based on real-world data, supporting personalized care. Artificial intelligence\u2013based approaches also show promise in streamlining clinical trial design and execution, leveraging on real-time data on patient responsiveness, enhancing recruitment efficiency. However, in order to fully realize these benefits, robust validation across diverse patient populations is necessary to ensure accuracy and generalizability. In addition, addressing concerns regarding data quality, privacy, and bias is equally critical to avoid exacerbating existing healthcare disparities. Scientific societies and regulatory agencies must ultimately establish standardized frameworks for data management, model certification, and transparency, to enable safe and effective integration of AI into clinical practice. This manuscript aims at systematically reviewing the current state-of-the-art applications of AI in cardiovascular pharmacotherapy, describing their current potential in guiding treatment decisions, refine trial methodologies and support drug discovery.<\/jats:p>","DOI":"10.1093\/eurheartj\/ehaf474","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T07:56:20Z","timestamp":1750233380000},"page":"3616-3627","source":"Crossref","is-referenced-by-count":28,"title":["Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives"],"prefix":"10.1093","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3097-2834","authenticated-orcid":false,"given":"Francesco","family":"Costa","sequence":"first","affiliation":[{"name":"Cardiology Department, University Hospital Virgen de la Victoria, Instituto de Investigaci\u00f3n Biom\u00e9dica de M\u00e1laga (IBIMA) , M\u00e1laga 29010 ,","place":["Spain"]},{"name":"Department of Biomedical and Dental Sciences and of Morphological and Functional Images, University of Messina , Messina 98122 ,","place":["Italy"]},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red en Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III , Madrid 28220 ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Jose","family":"Gomez Doblas","sequence":"additional","affiliation":[{"name":"Cardiology Department, University Hospital Virgen de la Victoria, Instituto de Investigaci\u00f3n Biom\u00e9dica de M\u00e1laga (IBIMA) , M\u00e1laga 29010 ,","place":["Spain"]},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red en Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III , Madrid 28220 ,","place":["Spain"]},{"name":"Department of Medicine and Dermatology, University of Malaga , M\u00e1laga ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arancha","family":"D\u00edaz Exp\u00f3sito","sequence":"additional","affiliation":[{"name":"Cardiology Department, University Hospital Virgen de la Victoria, Instituto de Investigaci\u00f3n Biom\u00e9dica de M\u00e1laga (IBIMA) , M\u00e1laga 29010 ,","place":["Spain"]},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red en Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III , Madrid 28220 ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3855-1815","authenticated-orcid":false,"given":"Marianna","family":"Adamo","sequence":"additional","affiliation":[{"name":"Institute of Cardiology, ASST Spedali Civili di Brescia, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia , Brescia ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6646-9317","authenticated-orcid":false,"given":"Fabrizio","family":"D\u2019Ascenzo","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Cardiovascular and Thoracic Department, Citt\u00e0 della Salute e della Scienza Hospital and University of Turin , Turin ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukasz","family":"Ko\u0142towski","sequence":"additional","affiliation":[{"name":"1st Department of Cardiology, Medical University of Warsaw , Warsaw ,","place":["Poland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Saba","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Cagliari , Cagliari ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6652-9036","authenticated-orcid":false,"given":"Guiomar","family":"Mendieta","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Hospital de la Santa Creu i Sant Pau , Barcelona ,","place":["Spain"]},{"name":"Centro Nacional de Investigaciones Cardiovasculares (CNIC) , Madrid ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felice","family":"Gragnano","sequence":"additional","affiliation":[{"name":"Department of Translational Medical Sciences, University of Campania \u2018Luigi Vanvitelli\u2019 , Caserta 81100 ,","place":["Italy"]},{"name":"Division of Clinical Cardiology, A.O.R.N. \u201cSant\u00b4Anna e San Sebastiano\u201d , Caserta 81100 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5018-830X","authenticated-orcid":false,"given":"Paolo","family":"Calabr\u00f2","sequence":"additional","affiliation":[{"name":"Department of Translational Medical Sciences, University of Campania \u2018Luigi Vanvitelli\u2019 , Caserta 81100 ,","place":["Italy"]},{"name":"Division of Clinical Cardiology, A.O.R.N. \u201cSant\u00b4Anna e San Sebastiano\u201d , Caserta 81100 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Badimon","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red en Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III , Madrid 28220 ,","place":["Spain"]},{"name":"Cardiovascular Program ICCC, Hospital de la Santa Creu i Sant Pau Research Institute , IIB-Sant Pau, CiberCV, Barcelona ,","place":["Spain"]},{"name":"Cardiovascular Research Chair, UAB , Barcelona ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Borja","family":"Iba\u00f1ez","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red en Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III , Madrid 28220 ,","place":["Spain"]},{"name":"Centro Nacional de Investigaciones Cardiovasculares (CNIC) , Madrid ,","place":["Spain"]},{"name":"Cardiology Department, Instituto de Investigaci\u00f3n Sanitaria-Fundaci\u00f3n Jim\u00e9nez D\u00edaz (IIS-FJD,UAM) , Madrid ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roxana","family":"Mehran","sequence":"additional","affiliation":[{"name":"Zena and Michael A. Wiener Cardiovascular Institute (R.M.), Icahn School of Medicine at Mount Sinai , New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominick J","family":"Angiolillo","sequence":"additional","affiliation":[{"name":"Division of Cardiology, University of Florida College of Medicine , Jacksonville, FL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"L\u00fcscher","sequence":"additional","affiliation":[{"name":"Royal Brompton & Harefield Hospitals and Cardiovascular Academic Group, King\u2019s College , London ,","place":["UK"]},{"name":"Center for Molecular Cardiology, University of Zurich , Zurich ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5156-7723","authenticated-orcid":false,"given":"Davide","family":"Capodanno","sequence":"additional","affiliation":[{"name":"A.O.U. Policlinico \u2018G. Rodolico\u2014San Marco\u2019, University of Catania , Catania 95123 ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"2025100219514621200_ehaf474-B1","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1186\/s40001-023-01065-y","article-title":"Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives","volume":"28","author":"Sun","year":"2023","journal-title":"Eur J Med Res"},{"key":"2025100219514621200_ehaf474-B2","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1038\/s41569-020-00503-2","article-title":"Artificial intelligence-enhanced electrocardiography in cardiovascular disease management","volume":"18","author":"Siontis","year":"2021","journal-title":"Nat Rev Cardiol"},{"key":"2025100219514621200_ehaf474-B3","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1038\/s41746-023-00966-w","article-title":"Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias","volume":"6","author":"Gadaleta","year":"2023","journal-title":"NPJ Digit Med"},{"key":"2025100219514621200_ehaf474-B4","doi-asserted-by":"publisher","first-page":"e220107","DOI":"10.1148\/ryct.220107","article-title":"Prognostic value of machine learning-based time-to-event analysis using coronary CT angiography in patients with suspected coronary artery disease","volume":"5","author":"Bauer","year":"2023","journal-title":"Radiol Cardiothorac Imaging"},{"key":"2025100219514621200_ehaf474-B5","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1016\/j.jacc.2023.06.040","article-title":"Association of coronary artery calcium detected by routine ungated CT imaging with cardiovascular outcomes","volume":"82","author":"Peng","year":"2023","journal-title":"J Am Coll Cardiol"},{"key":"2025100219514621200_ehaf474-B6","doi-asserted-by":"publisher","first-page":"2472","DOI":"10.1016\/j.jacc.2024.03.400","article-title":"Artificial intelligence for cardiovascular care-part 1: advances: JACC review topic of the week","volume":"83","author":"Elias","year":"2024","journal-title":"J Am Coll Cardiol"},{"key":"2025100219514621200_ehaf474-B7","doi-asserted-by":"publisher","first-page":"2020","DOI":"10.1016\/j.jcin.2022.08.040","article-title":"Enhanced diagnosis of plaque erosion by deep learning in patients with acute coronary syndromes","volume":"15","author":"Park","year":"2022","journal-title":"JACC Cardiovasc Interv"},{"key":"2025100219514621200_ehaf474-B8","doi-asserted-by":"publisher","first-page":"4291","DOI":"10.1093\/eurheartj\/ehae465","article-title":"Artificial intelligence in cardiovascular medicine: clinical applications","volume":"45","author":"L\u00fcscher","year":"2024","journal-title":"Eur Heart J"},{"key":"2025100219514621200_ehaf474-B9","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1038\/s41746-023-00869-w","article-title":"Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices","volume":"6","author":"Khunte","year":"2023","journal-title":"NPJ Digit Med"},{"key":"2025100219514621200_ehaf474-B10","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.jchf.2021.08.008","article-title":"Remote speech analysis in the evaluation of hospitalized patients with acute decompensated heart failure","volume":"10","author":"Amir","year":"2022","journal-title":"JACC Heart Fail"},{"key":"2025100219514621200_ehaf474-B11","doi-asserted-by":"publisher","first-page":"3912","DOI":"10.1093\/eurheartj\/ehae178","article-title":"2024 ESC guidelines for the management of elevated blood pressure and hypertension","volume":"45","author":"McEvoy","year":"2024","journal-title":"Eur Heart J"},{"key":"2025100219514621200_ehaf474-B12","doi-asserted-by":"publisher","first-page":"e009476","DOI":"10.1161\/JAHA.118.009476","article-title":"Machine learning outperforms ACC\/AHA CVD risk calculator in MESA","volume":"7","author":"Kakadiaris","year":"2018","journal-title":"J Am Heart Assoc"},{"key":"2025100219514621200_ehaf474-B13","doi-asserted-by":"publisher","first-page":"e200255","DOI":"10.1001\/jamanetworkopen.2020.0255","article-title":"Effect of home blood pressure monitoring via a smartphone hypertension coaching application or tracking application on adults with uncontrolled hypertension: a randomized clinical trial","volume":"3","author":"Persell","year":"2020","journal-title":"JAMA Netw Open"},{"key":"2025100219514621200_ehaf474-B14","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1161\/HYPERTENSIONAHA.124.22349","article-title":"Controversy in hypertension: pro-Side of the argument using artificial intelligence for hypertension diagnosis and management","volume":"82","author":"Armoundas","year":"2025","journal-title":"Hypertension"},{"key":"2025100219514621200_ehaf474-B15","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1161\/HYPERTENSIONAHA.122.20410","article-title":"The microsoft research Aurora project: important findings on cuffless blood pressure measurement","volume":"80","author":"Mukkamala","year":"2023","journal-title":"Hypertension"},{"key":"2025100219514621200_ehaf474-B16","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1016\/j.mcpdig.2023.10.001","article-title":"Causal deep neural network-based model for first-line hypertension management","volume":"1","author":"Herzog","year":"2023","journal-title":"Mayo Clin Proc Digit Health"},{"key":"2025100219514621200_ehaf474-B17","doi-asserted-by":"publisher","first-page":"e796","DOI":"10.1016\/S2589-7500(22)00170-4","article-title":"Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post hoc analysis of randomised clinical trials","volume":"4","author":"Oikonomou","year":"2022","journal-title":"Lancet Digit Health"},{"key":"2025100219514621200_ehaf474-B18","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1093\/ije\/dyad037","article-title":"Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management","volume":"52","author":"Inoue","year":"2023","journal-title":"Int J Epidemiol"},{"key":"2025100219514621200_ehaf474-B19","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.ejim.2023.05.021","article-title":"Evaluation of machine learning algorithms for renin-angiotensin-aldosterone system inhibitors associated renal adverse event prediction","volume":"114","author":"G\u00fcven","year":"2023","journal-title":"Eur J Intern Med"},{"key":"2025100219514621200_ehaf474-B20","doi-asserted-by":"publisher","first-page":"2209336","DOI":"10.1080\/07853890.2023.2209336","article-title":"Machine learning algorithms identify hypokalaemia risk in people with hypertension in the United States national health and nutrition examination survey 1999\u20132018","volume":"55","author":"Lin","year":"2023","journal-title":"Ann Med"},{"key":"2025100219514621200_ehaf474-B21","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1002\/pds.5176","article-title":"Using artificial intelligence to identify anti-hypertensives as possible disease modifying agents in Parkinson\u2019s disease","volume":"30","author":"Visanji","year":"2021","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"2025100219514621200_ehaf474-B22","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal Fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"2025100219514621200_ehaf474-B23","doi-asserted-by":"publisher","first-page":"15889","DOI":"10.1038\/s41598-022-20149-z","article-title":"Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data","volume":"12","author":"Seto","year":"2022","journal-title":"Sci Rep"},{"key":"2025100219514621200_ehaf474-B24","doi-asserted-by":"publisher","first-page":"104383","DOI":"10.1016\/j.ebiom.2022.104383","article-title":"Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: a machine learning analysis of population-based 10-year prospective cohort study","volume":"86","author":"Hahn","year":"2022","journal-title":"EBioMedicine"},{"key":"2025100219514621200_ehaf474-B25","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.neucom.2020.04.148","article-title":"Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy","volume":"452","author":"Martinez-Murcia","year":"2021","journal-title":"Neurocomputing (Amst)"},{"key":"2025100219514621200_ehaf474-B26","doi-asserted-by":"publisher","first-page":"1904-LB","DOI":"10.2337\/db24-1904-LB","article-title":"Glycemic improvement with use of the omnipod 5 automated insulin delivery system in adults with type 2 diabetes\u2014results of the SECURE-T2D pivotal trial","volume":"73","author":"Pasquel","year":"2024","journal-title":"Diabetes"},{"key":"2025100219514621200_ehaf474-B27","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1186\/s12933-023-01985-3","article-title":"Machine learning in precision diabetes care and cardiovascular risk prediction","volume":"22","author":"Oikonomou","year":"2023","journal-title":"Cardiovasc Diabetol"},{"key":"2025100219514621200_ehaf474-B28","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1089\/dia.2024.0041","article-title":"A scalable application of artificial intelligence-driven insulin titration program to transform type 2 diabetes management","volume":"26","author":"Warren","year":"2024","journal-title":"Diabetes Technol Ther"},{"key":"2025100219514621200_ehaf474-B29","doi-asserted-by":"publisher","first-page":"108106","DOI":"10.1016\/j.compbiomed.2024.108106","article-title":"Exploring metformin monotherapy response in type-2 diabetes: computational insights through clinical, genomic, and proteomic markers using machine learning algorithms","volume":"171","author":"Villikudathil","year":"2024","journal-title":"Comput Biol Med"},{"key":"2025100219514621200_ehaf474-B30","doi-asserted-by":"publisher","first-page":"965","DOI":"10.2337\/dc21-1765","article-title":"Phenomapping-derived tool to individualize the effect of canagliflozin on cardiovascular risk in type 2 diabetes","volume":"45","author":"Oikonomou","year":"2022","journal-title":"Diabetes Care"},{"key":"2025100219514621200_ehaf474-B31","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1186\/s12933-022-01496-7","article-title":"Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis","volume":"21","author":"Edward","year":"2022","journal-title":"Cardiovasc Diabetol"},{"key":"2025100219514621200_ehaf474-B32","doi-asserted-by":"publisher","first-page":"e2030913","DOI":"10.1001\/jamanetworkopen.2020.30913","article-title":"Development and validation of a machine learning model to predict near-term risk of iatrogenic hypoglycemia in hospitalized patients","volume":"4","author":"Mathioudakis","year":"2021","journal-title":"JAMA Netw Open"},{"key":"2025100219514621200_ehaf474-B33","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1002\/pds.5206","article-title":"Using machine learning to identify diabetes patients with canagliflozin prescriptions at high-risk of lower extremity amputation using real-world data","volume":"30","author":"Yang","year":"2021","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"2025100219514621200_ehaf474-B34","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1080\/03007995.2019.1706043","article-title":"Myocardial infarction in type 2 diabetes using sodium-glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: proportional hazards analysis by deep neural network based machine learning","volume":"36","author":"Yamada","year":"2020","journal-title":"Curr Med Res Opin"},{"key":"2025100219514621200_ehaf474-B35","doi-asserted-by":"publisher","first-page":"130375","DOI":"10.1016\/j.ijbiomac.2024.130375","article-title":"Exploring novel lead scaffolds for SGLT2 inhibitors: insights from machine learning and molecular dynamics simulations","volume":"263","author":"Yu","year":"2024","journal-title":"Int J Biol Macromol"},{"key":"2025100219514621200_ehaf474-B36","doi-asserted-by":"publisher","first-page":"108106","DOI":"10.1016\/j.jmgm.2021.108106","article-title":"Exploring sodium glucose cotransporter (SGLT2) inhibitors with machine learning approach: a novel hope in anti-diabetes drug discovery","volume":"111","author":"Moinul","year":"2022","journal-title":"J Mol Graph Model"},{"key":"2025100219514621200_ehaf474-B37","doi-asserted-by":"publisher","first-page":"107597","DOI":"10.1016\/j.compbiolchem.2021.107597","article-title":"Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure-activity relationship-based artificial intelligence and molecular docking of hit compounds","volume":"95","author":"Hermansyah","year":"2021","journal-title":"Comput Biol Chem"},{"key":"2025100219514621200_ehaf474-B38","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/S0149-2918(02)85140-2","article-title":"A preliminary evaluation of neural network analysis for pharmacodynamic modeling of the dosing of the hydroxymethylglutaryl coenzyme A-reductase inhibitors simvastatin and atorvastatin","volume":"24","author":"Moon","year":"2002","journal-title":"Clin Ther"},{"key":"2025100219514621200_ehaf474-B39","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1089\/met.2023.0009","article-title":"Machine learning applied to cholesterol-lowering pharmacotherapy: proof-of-concept in high-risk patients treated in primary care","volume":"21","author":"Krentz","year":"2023","journal-title":"Metab Syndr Relat Disord"},{"key":"2025100219514621200_ehaf474-B40","doi-asserted-by":"publisher","first-page":"104029","DOI":"10.1016\/j.jbi.2022.104029","article-title":"Producing personalized statin treatment plans to optimize clinical outcomes using big data and machine learning","volume":"128","author":"Chi","year":"2022","journal-title":"J Biomed Inform"},{"key":"2025100219514621200_ehaf474-B41","doi-asserted-by":"publisher","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 (Lond)"},{"key":"2025100219514621200_ehaf474-B42","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.ahj.2021.11.001","article-title":"Behavioral nudges as patient decision support for medication adherence: the ENCOURAGE randomized controlled trial","volume":"244","author":"Horne","year":"2022","journal-title":"Am Heart J"},{"key":"2025100219514621200_ehaf474-B43","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1038\/s44161-024-00469-1","article-title":"Development and multinational validation of an algorithmic strategy for high lp(a) screening","volume":"3","author":"Aminorroaya","year":"2024","journal-title":"Nat Cardiovasc Res"},{"key":"2025100219514621200_ehaf474-B44","doi-asserted-by":"publisher","first-page":"e004632","DOI":"10.1161\/CIRCGEN.124.004632","article-title":"Evaluation of a machine learning-guided strategy for elevated lipoprotein(a) screening in health systems","volume":"18","author":"Aminorroaya","year":"2025","journal-title":"Circ Genom Precis Med"},{"key":"2025100219514621200_ehaf474-B45","doi-asserted-by":"publisher","first-page":"71","DOI":"10.21037\/atm.2019.11.121","article-title":"Overview of clinical prediction models","volume":"8","author":"Chen","year":"2020","journal-title":"Ann Transl Med"},{"key":"2025100219514621200_ehaf474-B46","doi-asserted-by":"publisher","first-page":"22461","DOI":"10.1038\/s41598-023-49831-6","article-title":"Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea","volume":"13","author":"Choi","year":"2023","journal-title":"Sci Rep"},{"key":"2025100219514621200_ehaf474-B47","doi-asserted-by":"publisher","first-page":"14745","DOI":"10.1038\/s41598-021-94305-2","article-title":"Development of a system to support warfarin dose decisions using deep neural networks","volume":"11","author":"Lee","year":"2021","journal-title":"Sci Rep"},{"key":"2025100219514621200_ehaf474-B48","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1093\/ehjcvp\/pvz076","article-title":"New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF","volume":"6","author":"Goto","year":"2020","journal-title":"Eur Heart J Cardiovasc Pharmacother"},{"key":"2025100219514621200_ehaf474-B49","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1186\/s12877-023-04049-z","article-title":"XGBoost-based machine learning test improves the accuracy of hemorrhage prediction among geriatric patients with long-term administration of rivaroxaban","volume":"23","author":"Chen","year":"2023","journal-title":"BMC Geriatr"},{"key":"2025100219514621200_ehaf474-B50","doi-asserted-by":"publisher","first-page":"2359","DOI":"10.3390\/ijerph20032359","article-title":"Integrated machine learning decision tree model for risk evaluation in patients with non-valvular atrial fibrillation when taking different doses of dabigatran","volume":"20","author":"Huang","year":"2023","journal-title":"Int J Environ Res Public Health"},{"key":"2025100219514621200_ehaf474-B51","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1093\/ehjcvp\/pvaa135","article-title":"Antithrombotic therapy according to baseline bleeding risk in patients with atrial fibrillation undergoing percutaneous coronary intervention: applying the PRECISE-DAPT score in RE-DUAL PCI","volume":"8","author":"Costa","year":"2022","journal-title":"Eur Heart J Cardiovasc Pharmacother"},{"key":"2025100219514621200_ehaf474-B52","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1161\/CIRCOUTCOMES.116.003055","article-title":"Identifying drug-drug interactions by data mining: a pilot study of warfarin-associated drug interactions","volume":"9","author":"Hansen","year":"2016","journal-title":"Circ Cardiovasc Qual Outcomes"},{"key":"2025100219514621200_ehaf474-B53","doi-asserted-by":"publisher","first-page":"3720","DOI":"10.1093\/eurheartj\/ehad191","article-title":"2023 ESC guidelines for the management of acute coronary syndromes","volume":"44","author":"Byrne","year":"2023","journal-title":"Eur Heart J"},{"key":"2025100219514621200_ehaf474-B54","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1093\/eurheartj\/ehz425","article-title":"2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes","volume":"41","author":"Knuuti","year":"2020","journal-title":"Eur Heart J"},{"key":"2025100219514621200_ehaf474-B55","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1016\/S0140-6736(22)01483-0","article-title":"Sex-specific evaluation and redevelopment of the GRACE score in non-ST-segment elevation acute coronary syndromes in populations from the UK and Switzerland: a multinational analysis with external cohort validation","volume":"400","author":"Wenzl","year":"2022","journal-title":"Lancet"},{"key":"2025100219514621200_ehaf474-B56","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1093\/ehjcvp\/pvad063","article-title":"External validity of the PRECISE-DAPT score in patients undergoing PCI: a systematic review and meta-analysis","volume":"9","author":"Munaf\u00f2","year":"2023","journal-title":"Eur Heart J Cardiovasc Pharmacother"},{"key":"2025100219514621200_ehaf474-B57","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1093\/eurheartj\/ehx503","article-title":"Case-based implementation of the 2017 ESC focused update on dual antiplatelet therapy in coronary artery disease","volume":"39","author":"Collet","year":"2018","journal-title":"Eur Heart J"},{"key":"2025100219514621200_ehaf474-B58","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ahj.2020.01.014","article-title":"A 4-item PRECISE-DAPT score for dual antiplatelet therapy duration decision-making","volume":"223","author":"Costa","year":"2020","journal-title":"Am Heart J"},{"key":"2025100219514621200_ehaf474-B59","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1093\/ehjcvp\/pvaa106","article-title":"PRECISE-DAPT score for bleeding risk prediction in patients on dual or single antiplatelet regimens: insights from the GLOBAL LEADERS and GLASSY","volume":"8","author":"Gragnano","year":"2020","journal-title":"Eur Heart J Cardiovasc Pharmacother"},{"key":"2025100219514621200_ehaf474-B60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11239-019-01940-8","article-title":"Machine learning versus traditional risk stratification methods in acute coronary syndrome: a pooled randomized clinical trial analysis","volume":"49","author":"Gibson","year":"2020","journal-title":"J Thromb Thrombolysis"},{"key":"2025100219514621200_ehaf474-B61","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/S0140-6736(20)32519-8","article-title":"Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets","volume":"397","author":"D\u2019Ascenzo","year":"2021","journal-title":"Lancet"},{"key":"2025100219514621200_ehaf474-B62","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1093\/ehjqcco\/qcac002","article-title":"Safety and efficacy of different P2Y12 inhibitors in patients with acute coronary syndromes stratified by the PRAISE risk score: a multicentre study","volume":"8","author":"Patti","year":"2022","journal-title":"Eur Heart J Qual Care Clin Outcomes"},{"key":"2025100219514621200_ehaf474-B63","doi-asserted-by":"publisher","first-page":"3272","DOI":"10.1093\/cvr\/cvac013","article-title":"Global burden of heart failure: a comprehensive and updated review of epidemiology","volume":"118","author":"Savarese","year":"2023","journal-title":"Cardiovasc Res"},{"key":"2025100219514621200_ehaf474-B64","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1056\/NEJMoa1406656","article-title":"Demographic and epidemiologic drivers of global cardiovascular mortality","volume":"372","author":"Roth","year":"2015","journal-title":"N Engl J Med"},{"key":"2025100219514621200_ehaf474-B65","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1016\/j.jacc.2020.06.061","article-title":"Machine learning assessment of left ventricular diastolic function based on electrocardiographic features","volume":"76","author":"Kagiyama","year":"2020","journal-title":"J Am Coll Cardiol"},{"key":"2025100219514621200_ehaf474-B66","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1093\/ehjdh\/ztaa015","article-title":"Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features","volume":"2","author":"Kwon","year":"2021","journal-title":"Eur Heart J Digital Health"},{"key":"2025100219514621200_ehaf474-B67","doi-asserted-by":"publisher","first-page":"e009303","DOI":"10.1161\/CIRCIMAGING.119.009303","article-title":"Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert","volume":"12","author":"Asch","year":"2019","journal-title":"Circ: Cardiovascular Imaging"},{"key":"2025100219514621200_ehaf474-B68","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","article-title":"Fully automated echocardiogram interpretation in clinical practice","volume":"138","author":"Zhang","year":"2018","journal-title":"Circulation"},{"key":"2025100219514621200_ehaf474-B69","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1002\/ejhf.2155","article-title":"A machine learning risk score predicts mortality across the spectrum of left ventricular ejection fraction","volume":"23","author":"Greenberg","year":"2021","journal-title":"Eur J Heart Fail"},{"key":"2025100219514621200_ehaf474-B70","doi-asserted-by":"publisher","first-page":"e0165304","DOI":"10.1371\/journal.pone.0165304","article-title":"A novel and effective method for congestive heart failure detection and quantification using dynamic heart rate variability measurement","volume":"11","author":"Chen","year":"2016","journal-title":"PLoS One"},{"key":"2025100219514621200_ehaf474-B71","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1002\/ejhf.1628","article-title":"Improving risk prediction in heart failure using machine learning","volume":"22","author":"Adler","year":"2020","journal-title":"Eur J Heart Fail"},{"key":"2025100219514621200_ehaf474-B72","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1002\/ejhf.3115","article-title":"Artificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction","volume":"26","author":"Wu","year":"2024","journal-title":"Eur J Heart Fail"},{"key":"2025100219514621200_ehaf474-B73","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1002\/ejhf.1621","article-title":"Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis","volume":"22","author":"Segar","year":"2020","journal-title":"Eur J Heart Fail"},{"key":"2025100219514621200_ehaf474-B74","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1161\/CIRCULATIONAHA.114.010637","article-title":"Phenomapping for novel classification of heart failure with preserved ejection fraction","volume":"131","author":"Shah","year":"2015","journal-title":"Circulation"},{"key":"2025100219514621200_ehaf474-B75","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1136\/heartjnl-2019-315481","article-title":"Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning","volume":"106","author":"Hedman","year":"2020","journal-title":"Heart"},{"key":"2025100219514621200_ehaf474-B76","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1016\/j.jcmg.2021.04.010","article-title":"Deep-learning models for the echocardiographic assessment of diastolic dysfunction","volume":"14","author":"Pandey","year":"2021","journal-title":"JACC Cardiovasc Imaging"},{"key":"2025100219514621200_ehaf474-B77","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.echo.2018.12.002","article-title":"Contribution of cardiovascular reserve to prognostic categories of heart failure with preserved ejection fraction: a classification based on machine learning","volume":"32","author":"Przewlocka-Kosmala","year":"2019","journal-title":"J Am Soc Echocardiogr"},{"key":"2025100219514621200_ehaf474-B78","doi-asserted-by":"publisher","first-page":"104795","DOI":"10.1016\/j.ebiom.2023.104795","article-title":"Treatment response to spironolactone in patients with heart failure with preserved ejection fraction: a machine learning-based analysis of two randomized controlled trials","volume":"96","author":"Kresoja","year":"2023","journal-title":"eBioMedicine"},{"key":"2025100219514621200_ehaf474-B79","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.jchf.2022.09.009","article-title":"Prediction of left ventricular ejection fraction change following treatment with sacubitril\/valsartan","volume":"11","author":"Mohebi","year":"2023","journal-title":"JACC Heart Fail"},{"key":"2025100219514621200_ehaf474-B80","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1016\/S0140-6736(21)01638-X","article-title":"Redefining \u03b2-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis","volume":"398","author":"Karwath","year":"2021","journal-title":"The Lancet"},{"key":"2025100219514621200_ehaf474-B81","doi-asserted-by":"publisher","first-page":"4269","DOI":"10.1093\/eurheartj\/ehy712","article-title":"Novel endotypes in heart failure: effects on guideline-directed medical therapy","volume":"39","author":"Tromp","year":"2018","journal-title":"Eur Heart J"},{"key":"2025100219514621200_ehaf474-B82","doi-asserted-by":"publisher","first-page":"e50395","DOI":"10.7759\/cureus.50395","article-title":"Artificial intelligence in the early prediction of cardiogenic shock in acute heart failure or myocardial infarction patients: a systematic review and meta-analysis","volume":"15","author":"Popat","year":"2023","journal-title":"Cureus"},{"key":"2025100219514621200_ehaf474-B83","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1002\/clc.23532","article-title":"Predicting 90 DAY acute heart failure readmission and death using machine LEARNING-SUPPORTED decision analysis","volume":"44","author":"Sarijaloo","year":"2021","journal-title":"Clin Cardiol"},{"key":"2025100219514621200_ehaf474-B84","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.ijcard.2022.07.035","article-title":"Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure","volume":"365","author":"Austin","year":"2022","journal-title":"Int J Cardiol"},{"key":"2025100219514621200_ehaf474-B85","doi-asserted-by":"publisher","first-page":"e0219302","DOI":"10.1371\/journal.pone.0219302","article-title":"Artificial intelligence algorithm for predicting mortality of patients with acute heart failure","volume":"14","author":"Kwon","year":"2019","journal-title":"PLoS One"},{"key":"2025100219514621200_ehaf474-B86","doi-asserted-by":"publisher","first-page":"15","DOI":"10.34067\/KID.0005562022","article-title":"A machine learning model to predict diuretic resistance","volume":"4","author":"Mercier","year":"2023","journal-title":"Kidney360"},{"key":"2025100219514621200_ehaf474-B87","doi-asserted-by":"publisher","first-page":"e006513","DOI":"10.1161\/CIRCHEARTFAILURE.119.006513","article-title":"Continuous wearable monitoring analytics predict heart failure hospitalization","volume":"13","author":"Stehlik","year":"2020","journal-title":"Circ Heart Fail"},{"key":"2025100219514621200_ehaf474-B88","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1016\/j.jacep.2023.04.008","article-title":"Detecting paroxysmal atrial fibrillation from an electrocardiogram in Sinus rhythm","volume":"9","author":"Gruwez","year":"2023","journal-title":"JACC Clin Electrophysiol"},{"key":"2025100219514621200_ehaf474-B89","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1080\/13696998.2022.2102355","article-title":"Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England","volume":"25","author":"Hill","year":"2022","journal-title":"J Med Econ"},{"key":"2025100219514621200_ehaf474-B90","doi-asserted-by":"publisher","first-page":"e028483","DOI":"10.1161\/JAHA.122.028483","article-title":"Evaluation of quantitative decision-making for rhythm management of atrial fibrillation using tabular Q-learning","volume":"12","author":"Barrett","year":"2023","journal-title":"J Am Heart Assoc"},{"key":"2025100219514621200_ehaf474-B91","doi-asserted-by":"publisher","first-page":"768468","DOI":"10.3389\/fphys.2021.768468","article-title":"Artificial intelligence-driven algorithm for drug effect prediction on atrial fibrillation: an in silico population of models approach","volume":"12","author":"Sanchez de la Nava","year":"2021","journal-title":"Front Physiol"},{"key":"2025100219514621200_ehaf474-B92","first-page":"280","article-title":"Amiodarone pharmacovigilance through an intelligent electronic health record application","volume":"122","author":"Newgaard","year":"2023","journal-title":"WMJ"},{"key":"2025100219514621200_ehaf474-B93","doi-asserted-by":"publisher","first-page":"e43734","DOI":"10.2196\/43734","article-title":"Explainable machine learning techniques to predict amiodarone-induced thyroid dysfunction risk: multicenter, retrospective study with external validation","volume":"25","author":"Lu","year":"2023","journal-title":"J Med Internet Res"},{"key":"2025100219514621200_ehaf474-B94","doi-asserted-by":"publisher","first-page":"e0227324","DOI":"10.1371\/journal.pone.0227324","article-title":"Applications of machine learning in decision analysis for dose management for dofetilide","volume":"14","author":"Levy","year":"2019","journal-title":"PLoS One"},{"key":"2025100219514621200_ehaf474-B95","doi-asserted-by":"publisher","first-page":"e0201059","DOI":"10.1371\/journal.pone.0201059","article-title":"Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: a proof of concept study","volume":"13","author":"Attia","year":"2018","journal-title":"PLoS One"},{"key":"2025100219514621200_ehaf474-B96","doi-asserted-by":"publisher","first-page":"3948","DOI":"10.1093\/eurheartj\/ehab588","article-title":"Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome","volume":"42","author":"Prifti","year":"2021","journal-title":"Eur Heart J"},{"key":"2025100219514621200_ehaf474-B97","doi-asserted-by":"publisher","first-page":"49","DOI":"10.3390\/pharmaceutics15010049","article-title":"A guide to in silico drug design","volume":"15","author":"Chang","year":"2022","journal-title":"Pharmaceutics"},{"key":"2025100219514621200_ehaf474-B98","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.1038\/d41586-024-00753-x","article-title":"How AI is being used to accelerate clinical trials","volume":"627","author":"Hutson","year":"2024","journal-title":"Nature"},{"key":"2025100219514621200_ehaf474-B99","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1002\/wsbm.1337","article-title":"In silico methods for drug repurposing and pharmacology","volume":"8","author":"Hodos","year":"2016","journal-title":"Wiley Interdiscip Rev Syst Biol Med"},{"key":"2025100219514621200_ehaf474-B100","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s13321-021-00522-2","article-title":"GNINA 1.0: molecular docking with deep learning","volume":"13","author":"McNutt","year":"2021","journal-title":"J Cheminform"},{"key":"2025100219514621200_ehaf474-B101","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1186\/s12859-017-1960-x","article-title":"Utilizing random forest QSAR models with optimized parameters for target identification and its application to target-fishing server","volume":"18","author":"Lee","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2025100219514621200_ehaf474-B102","doi-asserted-by":"publisher","first-page":"5277","DOI":"10.3390\/molecules25225277","article-title":"Machine learning methods in drug discovery","volume":"25","author":"Patel","year":"2020","journal-title":"Molecules"},{"key":"2025100219514621200_ehaf474-B103","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1016\/j.drudis.2018.11.014","article-title":"Artificial intelligence in drug development: present status and future prospects","volume":"24","author":"Mak","year":"2019","journal-title":"Drug Discov Today"},{"key":"2025100219514621200_ehaf474-B104","doi-asserted-by":"publisher","first-page":"106324","DOI":"10.1016\/j.ejps.2022.106324","article-title":"CADD, AI and ML in drug discovery: a comprehensive review","volume":"181","author":"Vemula","year":"2023","journal-title":"Eur J Pharm Sci"},{"key":"2025100219514621200_ehaf474-B105","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"2025100219514621200_ehaf474-B106","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1038\/d41586-024-01383-z","article-title":"Major AlphaFold upgrade offers boost for drug discovery","volume":"629","author":"Callaway","year":"2024","journal-title":"Nature"},{"key":"2025100219514621200_ehaf474-B107","doi-asserted-by":"publisher","author":"Zambaldi","DOI":"10.48550\/arXiv.2409.08022"},{"key":"2025100219514621200_ehaf474-B108","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1038\/nm.4306","article-title":"The drug repurposing hub: a next-generation drug library and information resource","volume":"23","author":"Corsello","year":"2017","journal-title":"Nat Med"},{"key":"2025100219514621200_ehaf474-B109","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1038\/s41586-021-03430-5","article-title":"Evaluating eligibility criteria of oncology trials using real-world data and AI","volume":"592","author":"Liu","year":"2021","journal-title":"Nature"},{"key":"2025100219514621200_ehaf474-B110","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1038\/s41746-023-00963-z","article-title":"An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials","volume":"6","author":"Oikonomou","year":"2023","journal-title":"NPJ Digit Med"},{"key":"2025100219514621200_ehaf474-B111","doi-asserted-by":"publisher","first-page":"4808","DOI":"10.1093\/eurheartj\/ehae619","article-title":"Cardiovascular care with digital twin technology in the era of generative artificial intelligence","volume":"45","author":"Thangaraj","year":"2024","journal-title":"Eur Heart J"},{"key":"2025100219514621200_ehaf474-B112","doi-asserted-by":"publisher","first-page":"2176618","DOI":"10.1080\/08839514.2023.2176618","article-title":"Diversity, equity, and inclusion in artificial intelligence: an evaluation of guidelines","volume":"37","author":"Cachat-Rosset","year":"2023","journal-title":"Appl Artif Intell"},{"key":"2025100219514621200_ehaf474-B113","doi-asserted-by":"publisher","first-page":"e2345050","DOI":"10.1001\/jamanetworkopen.2023.45050","article-title":"Guiding principles to address the impact of algorithm bias on racial and ethnic disparities in health and health care","volume":"6","author":"Chin","year":"2023","journal-title":"JAMA Netw Open"},{"key":"2025100219514621200_ehaf474-B114","doi-asserted-by":"publisher","first-page":"ztaf030","DOI":"10.1101\/2024.10.08.24315035","article-title":"Automated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models","author":"Vasisht Shankar","year":"2025","journal-title":"Eur Heart J Digital Health"},{"key":"2025100219514621200_ehaf474-B115","doi-asserted-by":"publisher","first-page":"2070","DOI":"10.1093\/eurheartj\/ehy377","article-title":"Standardized classification and framework for reporting, interpreting, and analysing medication non-adherence in cardiovascular clinical trials: a consensus report from the Non-adherence Academic Research Consortium (NARC)","volume":"40","author":"Valgimigli","year":"2019","journal-title":"Eur Heart J"}],"container-title":["European Heart Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/eurheartj\/article-pdf\/46\/37\/3616\/63765118\/ehaf474.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/eurheartj\/article-pdf\/46\/37\/3616\/63765118\/ehaf474.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T23:51:53Z","timestamp":1759449113000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/eurheartj\/article\/46\/37\/3616\/8202687"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,15]]},"references-count":115,"journal-issue":{"issue":"37","published-online":{"date-parts":[[2025,7,15]]},"published-print":{"date-parts":[[2025,10,3]]}},"URL":"https:\/\/doi.org\/10.1093\/eurheartj\/ehaf474","relation":{},"ISSN":["0195-668X","1522-9645"],"issn-type":[{"value":"0195-668X","type":"print"},{"value":"1522-9645","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,10,1]]},"published":{"date-parts":[[2025,7,15]]}}}