How Machine Learning Will Transform Biomedicine
Author(s) -
Jeremy Goecks,
Vahid Jalili,
Laura M. Heiser,
Joe W. Gray
Publication year - 2020
Publication title -
cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 26.304
H-Index - 776
eISSN - 1097-4172
pISSN - 0092-8674
DOI - 10.1016/j.cell.2020.03.022
Subject(s) - biomedicine , machine learning , artificial intelligence , process (computing) , perspective (graphical) , biology , computer science , bioinformatics , operating system
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.
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