Artificial Intelligence and Mechanical Circulatory Support.

Journal: Heart failure clinics
Published Date:

Abstract

Advances in machine learning algorithms and computing power have fueled a rapid increase in artificial intelligence research in health care, including mechanical circulatory support. In this review, we highlight the needs for artificial intelligence in the mechanical circulatory support field and summarize existing artificial intelligence applications in 3 areas: identifying patients appropriate for mechanical circulatory support therapy, predicting risks after mechanical circulatory support device implantation, and monitoring for adverse events. We address the challenges of incorporating artificial intelligence in daily clinical practice and recommend demonstration of artificial intelligence tools' clinical efficacy, reliability, transparency, and equity to drive implementation.

Authors

  • Song Li
    Department of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
  • Gavin W Hickey
    Division of Cardiology, University of Pittsburgh School of Medicine, 200 Lothrop Street, PUH, 5B, Pittsburgh, PA 15213, USA. Electronic address: https://twitter.com/GavHick.
  • Matthew M Lander
    Cardiovascular Institute, Allegheny Health Network, 320 E North Avenue, Pittsburgh, PA 15212, USA. Electronic address: https://twitter.com/MattLanderMD.
  • Manreet K Kanwar
    Cardiovascular Institute at Allegheny Health Network, Pittsburgh, Pennsylvania.