Artificial AI can be applied to various types

Artificial intelligence (AI) aims to mimic human cognitive
functions. It is bringing a change in healthcare, powering it with high availability
of healthcare data and progressive analytics techniques. The paper surveys the
current status of AI applications in healthcare and discuss its future. AI can
be applied to various types of healthcare data. 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 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. 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.

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The above discussion suggests that AI devices mainly fall
into two major categories. The first category includes machine learning
techniques that analyse structured data such as imaging, genetic and EP data. The
second category includes natural language processing methods that extract information
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. 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.

Despite the increasingly rich AI literature in healthcare,
the research mainly concentrates around a few disease types: cancer, nervous
system disease and cardiovascular disease. Cardiology: Dilsizian and Siegel
discussed the potential application of the AI system to diagnose the heart
disease through cardiac image. 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. Early diagnoses can be potentially achieved 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.

We categorise them into three groups: the classical machine
learning techniques, the more recent deep learning techniques27 and the NLP
methods. Inputs to ML algorithms include patient ‘traits’ and sometimes medical
outcomes of interest. Patients’ medical outcomes are often collected in clinical
research. 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. Unsupervised learning is well known for
feature extraction, while supervised learning is suitable for predictive
modelling via building some relationships between the patient traits and the
outcome of interest. More recently, semi-supervised 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. Clustering
and principal component analysis are two major unsupervised learning methods. Clustering
groups subjects with similar traits together into clusters, without using the
outcome information. Popular clustering algorithms include k-means clustering,
hierarchical clustering and Gaussian mixture clustering. On the other hand,
supervised learning considers the subjects’ outcomes together with their
traits, and goes through a certain training process to determine the best
outputs associated with the inputs that are closest to the outcomes on average.
Usually, the output formulations vary with the outcomes of interest. The
outcome can be the probability of getting a particular clinical event, the
expected value of a disease level or the expected survival time.

Clearly, compared with unsupervised learning, super- vised
learning provides more clinically relevant results; hence AI applications in
healthcare most often use super- vised learning.

AI Applications in Stroke: Stroke is a common and frequently
occurring disease that affects more than 500 million people worldwide. Below we
summarise some of the relevant AI techniques in the three main areas of stroke
care: early disease prediction and diagnosis, treatment, as well as outcome
prediction and prognosis evaluation. For lack of judgement of early stroke
symptom, only a few patients could receive timely treatment. Once the movement
of the patient is significantly different from the normal pattern, an alert of
stroke would be activated and evaluated for treatment as soon as possible. Maninini
et al proposed a wearable device for collecting data about thermal/pathological
gaits for stroke prediction. Some studies have tried to apply ML methods to
neuroimaging data to assist with stroke diagnosis. Rehmeetal used SVM in
resting-state functional MRI data, by which endophenotypes of motor disability
after stroke were identified and classified. 50 SVM can correctly classify
patients with stroke with 87.6% accuracy. Griffis et al tried naïve Bayes
classification to identify stroke lesion in T1-weighted MRI.51 The result is
comparable with human expert manual lesion delineation. 53 ML methods have also
been applied to analyse CT scans from patients with stroke.

To better support clinical decision-making process, Zhang et
al proposed a model for predicting 3-month treatment outcome by analysing
physiological parameters during 48 hours after stroke using logistic
regression. Asadi et al compiled a database of clinical information of 107
patients with acute anterior or posterior circulation stroke who underwent
intra-arterial therapy. The authors analysed the data via artificial neural
network and SVM, and obtained prediction accuracy above 70%. They also used ML
techniques to identify factors influencing outcome in brain arteriovenous
malformation treated with endo- vascular embolization. Birkner et al used an
optimal algorithm to predict 30-day mortality and obtained more accurate
prediction than existing methods. King et al used SVM to predict stroke
mortality at discharge. Brain images have been analysed to predict the outcome
of stroke treatment. Siegel et al extracted functional connectivity from MRI
and functional MRI data, and used ridge regression and multitask learning for
cognitive deficiency prediction after stroke.

A successful AI system must possess the ML component for
handling structured data and the NLP component for mining unstructured texts. The
sophisticated algorithms then need to be trained through healthcare data before
the system can assist physicians with disease diagnosis and treatment
suggestions. The system includes both ML and NLP modules, and has made
promising progress in oncology. The system started to make impact on actual
clinical practices. The cloud-based CC-Cruiser in24 can be one prototype to
connect an AI system with the front-end data input and the back-end clinical
actions. The AI system then uses the patients’ data to come up with clinical
suggestions. To over- come the difficulty, the US FDA made the first attempt to
provide guidance for assessing AI systems. In order to work well, AI systems
need to be trained by data from clinical studies. Once an AI system gets
deployed after initial training with historical data, continuation of the data
supply becomes a crucial issue for further development and improvement of the
system.

Current healthcare environment does not provide incentives
for sharing data on the system.

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