Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

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  • Maarten van Smeden
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University , Universiteitsweg 100, 3584 CG Utrecht , The Netherlands
  • Georg Heinze
    Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna , Vienna , Austria
  • Ben Van Calster
    Department of Development and Regeneration , KU Leuven, Leuven , Belgium
  • Folkert W Asselbergs
    Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University , Utrecht , The Netherlands
  • Panos E Vardas
    Department of Cardiology, Heraklion University Hospital , Heraklion , Greece
  • Nico Bruining
    Department of Cardiology , Erasmus MC , Thorax Center, Rotterdam , The Netherlands
  • Peter de Jaegere
    Department of Cardiology , Erasmus MC, Thorax Center, Rotterdam , The Netherlands
  • Jason H Moore
    Department of Computational Biomedicine, Cedars-Sinai Medical Center , Los Angeles, CA , USA
  • Spiros Denaxas
    Health Data Research UK and Institute of Health Informatics, University College London , London , UK
  • Anne Laure Boulesteix
    Institute for Medical Information Processing, Biometry and Epidemiology , LMU Munich , Germany
  • Karel G M Moons
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University , Universiteitsweg 100, 3584 CG Utrecht , The Netherlands

Abstract

<jats:title>Abstract</jats:title><jats:p>The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With the introduction of such AI-based prediction model tools and software in cardiovascular patient care, the cardiovascular researcher and healthcare professional are challenged to understand the opportunities as well as the limitations of the AI-based predictions. In this article, we present 12 critical questions for cardiovascular health professionals to ask when confronted with an AI-based prediction model. We aim to support medical professionals to distinguish the AI-based prediction models that can add value to patient care from the AI that does not.</jats:p>

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