Artificial intelligence in healthcare: past, present and future

Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, Yongjun Wang, Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, Yongjun Wang

Abstract

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

Keywords: big data; deep learning; neural network; stroke; support vector machine.

Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
The data types considered in the artificial intelligence artificial (AI) literature. The comparison is obtained through searching the diagnosis techniques in the AI literature on the PubMed database.
Figure 2
Figure 2
The road map from clinical data generation to natural language processing data enrichment, to machine learning data analysis, to clinical decision making. EMR, electronic medical record; EP, electrophysiological.
Figure 3
Figure 3
The leading 10 disease types considered in the artificial intelligence (AI) literature. The first vocabularies in the disease names are displayed. The comparison is obtained through searching the disease types in the AI literature on PubMed.
Figure 4
Figure 4
Graphical illustration of unsupervised learning, supervised learning and semisupervised learning.
Figure 5
Figure 5
The machine learning algorithms used in the medical literature. The data are generated through searching the machine learning algorithms within healthcare on PubMed.
Figure 6
Figure 6
The machine learning algorithms used for imaging (upper), genetic (middle) and electrophysiological (bottom) data. The data are generated through searching the machine learning algorithms for each data type on PubMed.
Figure 7
Figure 7
An illustration of the support vector machine.
Figure 8
Figure 8
An illustration of neural network.
Figure 9
Figure 9
An illustration of deep learning with two hidden layers.
Figure 10
Figure 10
Current trend for deep learning. The data are generated through searching the deep learning in healthcare and disease category on PubMed.
Figure 11
Figure 11
The data sources for deep learning. The data are generated through searching deep learning in combination with the diagnosis techniques on PubMed.
Figure 12
Figure 12
The four main deep learning algorithm and their popularities. The data are generated through searching algorithm names in healthcare and disease category on PubMed.

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