How Machine Learning Will Transform Biomedicine.

Journal: Cell
Published Date: Apr 2, 2020

Abstract

This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health through a range of diseases and the normal aging process. For each area, early instances of successful machine learning applications are discussed, as well as opportunities and challenges for machine learning. When these challenges are met, machine learning promises a future of rigorous, outcomes-based medicine with detection, diagnosis, and treatment strategies that are continuously adapted to individual and environmental differences.

Authors

  • Jeremy Goecks
    Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA. Electronic address: goecksj@ohsu.edu.
  • Vahid Jalili
    Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
  • Laura M Heiser
    Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
  • Joe W Gray
    Department of Biomedical Engineering and the Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, USA.

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