Widespread application of artificial intelligence in health care has been anticipated for half a century. For most of that time, the dominant approach to artificial intelligence was inspired by logic: researchers assumed that the essence of intelligence was manipulating symbolic expressions, using rules of inference. This approach produced expert systems and graphical models that attempted to automate the reasoning processes of experts. In the last decade, however, a radically different approach to artificial intelligence, called deep learning, has produced major breakthroughs and is now used on billions of digital devices for complex tasks such as speech recognition, image interpretation, and language translation. The purpose of this Viewpoint is to give health care professionals an intuitive understanding of the technology underlying deep learning. In an accompanying Viewpoint, Naylor1 outlines some of the factors propelling adoption of this technology in medicine and health care.
[1]
Geoffrey E. Hinton,et al.
Learning representations by back-propagating errors
,
1986,
Nature.
[2]
Geoffrey E. Hinton,et al.
Learning representations by back-propagation errors, nature
,
1986
.
[3]
Geoffrey E. Hinton,et al.
Deep Learning
,
2015,
Nature.
[4]
Sebastian Thrun,et al.
Dermatologist-level classification of skin cancer with deep neural networks
,
2017,
Nature.
[5]
Michael V. McConnell,et al.
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
,
2017,
Nature Biomedical Engineering.
[6]
Jeffrey Dean,et al.
Scalable and accurate deep learning with electronic health records
,
2018,
npj Digital Medicine.
[7]
C. Naylor,et al.
On the Prospects for a (Deep) Learning Health Care System
,
2018,
JAMA.