TY - JOUR
T1 - How Machine Learning Will Transform Biomedicine
AU - Goecks, Jeremy
AU - Jalili, Vahid
AU - Heiser, Laura M.
AU - Gray, Joe W.
N1 - Funding Information:
J.G. and J.W.G. are supported by National Cancer Institute ( U2C CA233280 ). J.G. is supported by National Cancer Institute ( U24 CA231877 ). L.M.H. and J.W.G. are supported by the National Institutes of Health ( U54 HG008100 ) and National Cancer Institute ( U54 CA209988 ). J.G., L.M.H., and J.W.G. are supported by the Prospect Creek Foundation . J.W.G. is supported by the Susan G. Komen Foundation . L.M.H. is supported by the Breast Cancer Research Foundation and The Jayne Koskinas Ted Giovanis Foundation .
Funding Information:
J.G. and J.W.G. are supported by National Cancer Institute (U2C CA233280). J.G. is supported by National Cancer Institute (U24 CA231877). L.M.H. and J.W.G. are supported by the National Institutes of Health (U54 HG008100) and National Cancer Institute (U54 CA209988). J.G. L.M.H. and J.W.G. are supported by the Prospect Creek Foundation. J.W.G. is supported by the Susan G. Komen Foundation. L.M.H. is supported by the Breast Cancer Research Foundation and The Jayne Koskinas Ted Giovanis Foundation. J.W.G. receives research support from Micron and ThermoFisher and has stock in NVIDIA, Microsoft, Amazon, Google (Alphabet), and GE.
Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2020/4/2
Y1 - 2020/4/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85082406185&partnerID=8YFLogxK
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U2 - 10.1016/j.cell.2020.03.022
DO - 10.1016/j.cell.2020.03.022
M3 - Review article
C2 - 32243801
AN - SCOPUS:85082406185
SN - 0092-8674
VL - 181
SP - 92
EP - 101
JO - Cell
JF - Cell
IS - 1
ER -