TY - JOUR
T1 - Artificial Intelligence Transforms the Future of Health Care
AU - Noorbakhsh-Sabet, Nariman
AU - Zand, R.
AU - Zhang, Yanfei
AU - Abedi, Vida
N1 - Funding Information:
Funding: This work was in part supported by Geisinger Research, and by funds from the National Institute of Health (NIH) grant No. R56HL116832 to Sutter Health and sub-awarded to VA (Sub-PI, Geisinger) as well as funds from the Defense Threat Reduction Agency (DTRA) grant No. HDTRA1-18-1-0008 to Virginia Tech and sub-awarded to VA (Sub-PI, Geisinger, sub-award No. 450557-19D03). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Funding Information:
Funding: This work was in part supported by Geisinger Research, and by funds from the National Institute of Health (NIH) grant No. R56HL116832 to Sutter Health and sub-awarded to VA (Sub-PI, Geisinger) as well as funds from the Defense Threat Reduction Agency (DTRA) grant No. HDTRA1-18-1-0008 to Virginia Tech and sub-awarded to VA (Sub-PI, Geisinger, sub-award No. 450557-19D03). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/7
Y1 - 2019/7
N2 - Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data are available than ever, only a fraction is being curated, integrated, understood, and analyzed. AI focuses on how computers learn from data and mimic human thought processes. AI increases learning capacity and provides decision support system at scales that are transforming the future of health care. This article is a review of applications for machine learning in health care with a focus on clinical, translational, and public health applications with an overview of the important role of privacy, data sharing, and genetic information.
AB - Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data are available than ever, only a fraction is being curated, integrated, understood, and analyzed. AI focuses on how computers learn from data and mimic human thought processes. AI increases learning capacity and provides decision support system at scales that are transforming the future of health care. This article is a review of applications for machine learning in health care with a focus on clinical, translational, and public health applications with an overview of the important role of privacy, data sharing, and genetic information.
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U2 - 10.1016/j.amjmed.2019.01.017
DO - 10.1016/j.amjmed.2019.01.017
M3 - Review article
C2 - 30710543
AN - SCOPUS:85062290450
VL - 132
SP - 795
EP - 801
JO - American Journal of Medicine
JF - American Journal of Medicine
SN - 0002-9343
IS - 7
ER -
