Artificial Intelligence in Healthcare: Past, Present and Future

230 Jiang f, et al. Stroke and Vascular Neurology 2017;2:e000101. doi:10.1136/svn-2017-000101 Open Access
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.
Overview Of the medicAl ArtificiAl
intelligence (Ai) reseArch
Recently AI techniques have sent vast waves
across healthcare, even fuelling an active
discussion of whether AI doctors will eventu-
ally replace human physicians in the future.
We believe that human physicians will not
be replaced by machines in the foreseeable
future, but AI can definitely assist physicians to
make better clinical decisions or even replace
human judgement in certain functional areas
of healthcare (eg, radiology). The increasing
availability of healthcare data and rapid devel-
opment of big data analytic methods has
made possible the recent successful applica-
tions of AI in healthcare. Guided by relevant
clinical questions, powerful AI techniques can
unlock clinically relevant information hidden
in the massive amount of data, which in turn
can assist clinical decision making.1–3
In this article, we survey the current status
of AI in healthcare, as well as discuss its future.
We first briefly review four relevant aspects
from medical investigators’ perspectives:
1. motivations of applying AI in healthcare
2. data types that have be analysed by AI sys-
tems
3. mechanisms that enable AI systems to gen-
erate clinical meaningful results
4. disease types that the AI communities are
currently tackling.motivation
The advantages of AI have been extensively
discussed in the medical literature. 3–5 AI
can use sophisticated algorithms to ‘learn’
features from a large volume of healthcare
data, and then use the obtained insights to
assist clinical practice. It can also be equipped
with learning and self-correcting abilities to
improve its accuracy based on feedback. An
AI system can assist physicians by providing
up-to-date medical information from jour-
nals, textbooks and clinical practices to
inform proper patient care.6 In addition, an
AI system can help to reduce diagnostic and
therapeutic errors that are inevitable in the
human clinical practice.3 4 6–10 Moreover, an
AI system extracts useful information from
a large patient population to assist making
real-time inferences for health risk alert and
health outcome prediction.11healthcare data
Before AI systems can be deployed in health-
care applications, they need to be ‘trained’
through data that are generated from clin-
ical activities, such as screening, diagnosis,
treatment assignment and so on, so that they
can learn similar groups of subjects, associa-
tions between subject features and outcomes
of interest. These clinical data often exist in
but not limited to the form of demographics,
medical notes, electronic recordings from
medical devices, physical examinations and
clinical laboratory and images.12
Specifically, in the diagnosis stage, a substan-
tial proportion of the AI literature analyses
data from diagnosis imaging, genetic testing
and electrodiagnosis (figure 1). For example,
Jha and Topol urged radiologists to adopt
AI technologies when analysing diagnostic
images that contain vast data information.13
Li et al studied the uses of abnormal genetic
Artificial intelligence in healthcare: past,
present and future
Fei Jiang,1 Yong Jiang,2 Hui Zhi,3 Yi Dong,4 Hao Li,5 Sufeng Ma,6 Yilong Wang,7
Qiang Dong,4 Haipeng Shen,8 Yongjun Wang9
1
Department of Statistics and
Actuarial Sciences, University of
Hong Kong, Hong Kong, China
2
Department of Neurology,
Beijing Tiantan Hospital, Capital
Medical University, Beijing,
China
3
Biostatistics and Clinical
Research Methodology Unit,
University of Hong Kong Li Ka
Shing Faculty of Medicine, Hong
Kong, China
4
Department of Neurology,
Huashan Hospital, Fudan
University, Shanghai, China
5
China National Clinical
Research Center for
Neurological Diseases, Beijing,
China
6
DotHealth, Shanghai, China
7
Department of Neurology,
Tiantan Clinical Trial and
Research Center for Stroke,
Beijing, China
8
Faculty of Business and
Economics, University of Hong
Kong, Hong Kong, China
9
Department of Neurology,
Beijing Tiantan Hospital, Beijing,
Chinacorrespondence to
Prof Yongjun Wang;
yongjunwang1962@ gmail. com
To cite: Jiang F, Jiang Y, Zhi H,
et al. Artificial intelligence in
healthcare: past, present and
future. Stroke and Vascular
Neurology 2017;2: e000101.
doi:10.1136/svn-2017-000101
Received 12 June 2017
Accepted 14 June 2017
Published Online First
22 June 2017
Review
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231Jiang f, et al. Stroke and Vascular Neurology 2017;2:e000101. doi:10.1136/svn-2017-000101
Open Access
expression in long non-coding RNAs to diagnose gastric
cancer.14 Shin et al developed an electrodiagnosis support
system for localising neural injury.15
In addition, physical examination notes and clinical
laboratory results are the other two major data sources
(figure 1). We distinguish them with image, genetic and
electrophysiological (EP) data because they contain large
portions of unstructured narrative texts, such as clin-
ical notes, that are not directly analysable. As a conse-
quence, the corresponding AI applications focus on first
converting the unstructured text to machine-understand-
able electronic medical record (EMR). For example,
Karakülah et al used AI technologies to extract pheno-
typic features from case reports to enhance the diagnosis
accuracy of the congenital anomalies.16
Ai devices
The above discussion suggests that AI devices mainly fall
into two major categories. The first category includes
machine learning (ML) techniques that analyse struc-
tured data such as imaging, genetic and EP data. In
the medical applications, the ML procedures attempt
to cluster patients’ traits, or infer the probability of the
disease outcomes.17 The second category includes natural
language processing (NLP) methods that extract infor-
mation from unstructured data such as clinical notes/
medical journals to supplement and enrich structured
medical data. The NLP procedures target at turning texts
to machine-readable structured data, which can then be
analysed by ML techniques.18
For better presentation, the flow chart in figure 2
describes the road map from clinical data generation,
through NLP data enrichment and ML data analysis, to
clinical decision making. We comment that the road map
starts and ends with clinical activities. As powerful as AI
techniques can be, they have to be motivated by clinical
problems and be applied to assist clinical practice in the
end.disease focus
Despite the increasingly rich AI literature in healthcare,
the research mainly concentrates around a few disease
types: cancer, nervous system disease and cardiovascular
disease (figure 3). We discuss several examples below.
1. Cancer: Somashekhar et al demonstrated that the IBM
Watson for oncology would be a reliable AI system for
assisting the diagnosis of cancer through a double-
blinded validation study. 19 Esteva et al analysed clinical
images to identify skin cancer subtypes.20
2. Neurology: Bouton et al developed an AI system to
restore the control of movement in patients with
quadriplegia.21 Farina et al tested the power of an of-
fline man/machine interface that uses the discharge
timings of spinal motor neurons to control upper-limb
prostheses.22
3. Cardiology: Dilsizian and Siegel discussed the
potential application of the AI system to diagnose
the heart disease through cardiac image. 3 Arterys
recently received clearance from the US Food and
Drug Administration (FDA) to market its Arterys
Cardio DL application, which uses AI to provide
automated, editable ventricle segmentations based on
conventional cardiac MRI images. 23
The concentration around these three diseases is not
completely unexpected. All three diseases are leading
causes of death; therefore, early diagnoses are crucial
to prevent the deterioration of patients’ health status.
Furthermore, early diagnoses can be potentially achieved
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.
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232Jiang f, et al. Stroke and Vascular Neurology 2017;2:e000101. doi:10.1136/svn-2017-000101
Open Access
through improving the analysis procedures on imaging,
genetic, EP or EMR, which is the strength of the AI system.
Besides the three major diseases, AI has been applied
in other diseases as well. Two very recent examples were
Long et al, who analysed the ocular image data to diag-
nose congenital cataract disease,24 and Gulshan et al,
who detected referable diabetic retinopathy through the
retinal fundus photographs.25
The rest of the paper is organised as follows. In section
2, we describe popular AI devices in ML and NLP; the
ML techniques are further grouped into classical tech-
niques and the more recent deep learning. Section 3
focuses on discussing AI applications in neurology, from
the three aspects of early disease prediction and diagnosis,
treatment, outcome prediction and prognosis evaluation.
We then conclude in section 4 with some discussion about
the future of AI in healthcare.the Ai devices: ml And nlP
In this section, we review the AI devices (or techniques)
that have been found useful in the medial applications.
We categorise them into three groups: the classical
machine learning techniques, 26 the more recent deep
learning techniques 27 and the NLP methods.28classical ml
ML constructs data analytical algorithms to extract
features from data. Inputs to ML algorithms include
patient ‘traits’ and sometimes medical outcomes of
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.
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233Jiang f, et al. Stroke and Vascular Neurology 2017;2:e000101. doi:10.1136/svn-2017-000101
Open Access
interest. A patient’s traits commonly include baseline
data, such as age, gender, disease history and so on, and
disease-specific data, such as diagnostic imaging, gene
expressions, EP test, physical examination results, clin-
ical symptoms, medication and so on. Besides the traits,
patients’ medical outcomes are often collected in clin-
ical research. These include disease indicators, patient’s
survival times and quantitative disease levels, for example,
tumour sizes. To fix ideas, we denote the jth trait of the ith
patient by Xij, and the outcome of interest by Yi.
Depending on whether to incorporate the outcomes,
ML algorithms can be divided into two major categories:
unsupervised learning and supervised learning. Unsuper-
vised learning is well known for feature extraction, while
supervised learning is suitable for predictive modelling
via building some relationships between the patient traits
(as input) and the outcome of interest (as output). More
recently, semisupervised learning has been proposed
as a hybrid between unsupervised learning and super-
vised learning, which is suitable for scenarios where the
outcome is missing for certain subjects. These three types
of learning are illustrated in figure 4.
Clustering and principal component analysis (PCA)
are two major unsupervised learning methods. Clustering
groups subjects with similar traits together into clusters,
without using the outcome information. Clustering algo-
rithms output the cluster labels for the patients through
maximising and minimising the similarity of the patients
within and between the clusters. Popular clustering algo-
rithms include k-means clustering, hierarchical clustering
and Gaussian mixture clustering. PCA is mainly for dimen-
sion reduction, especially when the trait is recorded in a
large number of dimensions, such as the number of genes
in a genome-wide association study. PCA projects the data
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 Graphical illustration of unsupervised learning, supervised learning and semisupervised learning.
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