Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.

Journal: European heart journal
Published Date:

Abstract

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.

Authors

  • Simrat K Gill
    Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK.
  • Andreas Karwath
    Institute of Cancer and Genomic Sciences, University of Birmingham, UK; University Hospitals Birmingham NHS Foundation Trust, UK; Health Data Research, UK.
  • Hae-Won Uh
    Department of Biostatistics and Research Support, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands. h.w.uh@umcutrecht.nl.
  • Victor Roth Cardoso
    College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.
  • Zhujie Gu
    Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
  • Andrey Barsky
    Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • Luke Slater
    Computational Bioscience Research Center, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Saudi Arabia. luke.slater@kaust.edu.sa.
  • Animesh Acharjee
    College of Medicine and Health, School of Medical Sciences, Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
  • Jinming Duan
    School of Computer Science University of Birmingham Birmingham UK.
  • Lorenzo Dall'Olio
    Department of Physics and Astronomy, University of Bologna, 40126, Bologna, BO, Italy.
  • Said El Bouhaddani
    Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
  • Saisakul Chernbumroong
  • Mary Stanbury
    Patient and Public Involvement Team, Birmingham, UK.
  • Sandra Haynes
    Patient and Public Involvement Team, Birmingham, UK.
  • Folkert W Asselbergs
  • Diederick E Grobbee
    Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
  • Marinus J C Eijkemans
    Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
  • Georgios V Gkoutos
    Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, United Kingdom; Institute of Translational Medicine, University of Birmingham, Birmingham, United Kingdom; NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, United Kingdom; MRC Health Data Research UK (HDR UK), London, United Kingdom; NIHR Experimental Cancer Medicine Centre, Birmingham, United Kingdom; NIHR Biomedical Research Centre, University Hospital Birmingham, Birmingham, United Kingdom.
  • Dipak Kotecha
    University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK; Health Data Research UK Midlands Site, Birmingham, UK. Electronic address: d.kotecha@bham.ac.uk.