Canadian Journal of Cardiology

Digital Technologies: Revolutionizing Cardiovascular Medicine and Reshaping the World

  • Stanley Nattel
    Corresponding author: Dr Stanley Nattel, 5000 Belanger St, Montreal, Quebec H1T 1C8, Canada.
    Department of Medicine and Research Center, Montreal Heart Institute and Université de Montréal, Montreal, Quebec, Canada
    Institute of Pharmacology, West German Heart and Vascular Center, University Duisburg-Essen, Essen, Germany
    IHU LIRYC and Fondation Bordeaux Université, Bordeaux, France
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Published:December 22, 2021DOI:
      The phrase “The Moving Finger writes” has come to connote the irreversibility and consequences of human actions, when done. But of course, a finger is a digit and digital technologies, after having been introduced and developed, are causing inevitable and exponential changes in the world, with dramatic consequences—many extremely beneficial but others potentially terrifying.
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        • Gupta S.
        • Ko D.T.
        • Azizi P.
        • et al.
        Evaluation of machine learning algorithms for predicting readmission after acute myocardial infarction using routinely collected clinical data.
        Can J Cardiol. 2020; 36: 878-885
        • Cho S.M.
        • Austin P.C.
        • Ross H.J.
        • et al.
        Machine learning compared with conventional statistical models for predicting myocardial infarction readmission and mortality: a systematic review.
        Can J Cardiol. 2021; 37: 1207-1214
        • Weaver C.G.W.
        • McAlister F.A.
        Machine learning, predictive analytics, and the Emperor’s new clothes: why artificial intelligence has not yet replaced conventional approaches.
        Can J Cardiol. 2021; 37: 1156-1158
        • Miller D.D.
        Machine intelligence for management of acute coronary syndromes: neural or nervous times?.
        Can J Cardiol. 2020; 36: 470-473
        • Iannattone P.A.
        • Zhao X.
        • VanHouten J.
        • Garg A.
        • Huynh T.
        Artificial intelligence for diagnosis of acute coronary syndromes: a meta-analysis of machine learning approaches.
        Can J Cardiol. 2020; 36: 577-583
        • Leasure M.
        • Jain U.
        • Butchy A.
        • et al.
        Deep learning algorithm predicts angiographic coronary artery disease in stable patients using only a standard 12-lead electrocardiogram.
        Can J Cardiol. 2021; 37: 1715-1724
        • Avram R.
        • Olgin J.E.
        • Tison G.H.
        The rise of open-sourced machine learning in small and imbalanced data sets: predicting in-stent restenosis.
        Can J Cardiol. 2020; 36: 1574-1576
        • Sampedro-Gómez J.
        • Dorado-Díaz P.I.
        • Vicente-Palacios V.
        • et al.
        Machine learning to predict stent restenosis based on daily demographic, clinical, and angiographic characteristics.
        Can J Cardiol. 2020; 36: 1624-1632
        • Chang K.C.
        • Hsieh P.H.
        • Wu M.Y.
        • et al.
        Usefulness of machine learning-based detection and classification of cardiac arrhythmias with 12-lead electrocardiograms.
        Can J Cardiol. 2021; 37: 94-104
        • Zhou S.
        • Sapp J.L.
        • AbdelWahab A.
        • Trayanova N.
        Deep learning applied to electrocardiogram interpretation.
        Can J Cardiol. 2021; 37: 17-18
        • Bhattacharya M.
        • Lu D.Y.
        • Ventoulis I.
        • et al.
        Machine learning methods for identifying atrial fibrillation cases and their predictors in patients with hypertrophic cardiomyopathy: the HCM-AF-Risk Model.
        CJC Open. 2021; 3: 801-813
        • Adams S.J.
        • Haddad H.
        Artificial intelligence to diagnose heart failure based on chest x-rays and potential clinical implications.
        Can J Cardiol. 2021; 37: 1153-1155
        • Hirata Y.
        • Kusunose K.
        • Tsuji T.
        • Fujimori K.
        • Kotoku J.
        • Sata M.
        Deep learning for detection of elevated pulmonary artery wedge pressure using standard chest x-ray.
        Can J Cardiol. 2021; 37: 1198-1206
        • Tison G.H.
        • Avram R.
        • Nah G.
        • et al.
        Predicting incident heart failure in women with machine learning: the Women’s Health Initiative Cohort.
        Can J Cardiol. 2021; 37: 1708-1714
        • Moayedi Y.
        • Hershman S.G.
        • Henricksen E.J.
        • et al.
        Remote Mobile Outpatient Monitoring in Heart Transplant (ReBOOT): a pilot study.
        Can J Cardiol. 2020; 36: 1978.e9-10
        • Kitsiou S.
        • Vatani H.
        • Paré G.
        • et al.
        Effectiveness of mobile health technology interventions for patients with heart failure: systematic review and meta-analysis.
        Can J Cardiol. 2021; 37: 1248-1259
        • Moayedi Y.
        • Ross H.J.
        Seizing opportunities in mobile health technologies and heart failure: empowering patients and informing clinicians.
        Can J Cardiol. 2021; 37: 1163-1164
        • Padwal R.
        • Wood P.W.
        Digital health approaches for the assessment and optimization of hypertension care provision.
        Can J Cardiol. 2021; 37: 711-721
        • Superina S.
        • Malik A.
        • Moayedi Y.
        • McGillion M.
        • Ross H.J.
        Digital health: the promise and peril.
        Can J Cardiol. 2022; 38: 145-148
        • Manlhiot C.
        • Van den Eynde J.
        • Kutty S.
        • Ross H.J.
        A primer on the present state and future prospects for machine learning and artificial intelligence applications in cardiology.
        Can J Cardiol. 2022; 38: 169-184
        • Krittanawong C.
        • Aydar M.
        • Virk H.U.H.
        • et al.
        Artificial intelligence-powered blockchains for cardiovascular medicine.
        Can J Cardiol. 2022; 38: 185-195
        • Vervoort D.
        • Tam D.Y.
        • Wijeysundera H.C.
        Health technology assessment for cardiovascular digital health technologies and artificial intelligence: why is it different?.
        Can J Cardiol. 2022; 38: 259-266
        • Petch J.
        • Di S.
        • Nelson W.
        Opening the black box: the promise and limitations of explainable machine learning in cardiology.
        Can J Cardiol. 2022; 38: 204-213
        • Lang M.
        • Bernier A.
        • Knoppers B.M.
        AI in cardiovascular imaging: “unexplainable” legal and ethical challenges?.
        Can J Cardiol. 2022; 38: 225-233
        • Lauzier P.T.
        • Avram R.
        • Dey D.
        • Slomka P.
        • Afilalo J.
        • Chow B.J.W.
        The evolving role of artificial intelligence in cardiac image analysis.
        Can J Cardiol. 2022; 38: 214-224
        • Skandarani Y.
        • Lalande A.
        • Afilalo J.
        • Jodoin P.-M.
        Generative adversarial networks in cardiology.
        Can J Cardiol. 2022; 38: 196-203
        • Glass C.
        • Lafata K.J.
        • Jeck W.
        • et al.
        The role of machine learning in cardiovascular pathology.
        Can J Cardiol. 2022; 38: 234-245
        • Ng B.
        • Nayyar S.
        • Chauhan V.S.
        The role of artificial intelligence and machine learning in clinical cardiac electrophysiology.
        Can J Cardiol. 2022; 38: 246-258
        • McGillion M.H.
        • Allan K.
        • Ross-Howe S.
        • et al.
        Beyond wellness monitoring: continuous multiparameter remote automated monitoring of patients.
        Can J Cardiol. 2022; 38: 267-278
        • Brahmbhatt D.H.
        • Ross H.J.
        • Moayedi Y.
        Digital technology application for improved responses to healthcare challenges: lessons learned from COVID-19.
        Can J Cardiol. 2022; 38: 279-291