Application of Artificial Intelligence in Healthcare
Application of Artificial Intelligence in Healthcare
Application of Artificial Intelligence in Healthcare
DIGITAL TECHNOLOGY The potential for artificial intelligence in healthcare
Authors: Thomas Davenport A and Ravi Kalakota B
The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.
KEYWORDS : Artifi cial intelligence , clinical decision support ,
electronic health record systems
Introduction
Artificial intelligence (AI) and related technologies are increasingly
prevalent in business and society, and are beginning to be applied
to healthcare. These technologies have the potential to transform
many aspects of patient care, as well as administrative processes
within provider, payer and pharmaceutical organisations.
There are already a number of research studies suggesting that
AI can perform as well as or better than humans at key healthcare
tasks, such as diagnosing disease. Today, algorithms are already
outperforming radiologists at spotting malignant tumours, and
guiding researchers in how to construct cohorts for costly clinical
trials. However, for a variety of reasons, we believe that it will be
many years before AI replaces humans for broad medical process
domains. In this article, we describe both the potential that AI
offers to automate aspects of care and some of the barriers to
rapid implementation of AI in healthcare.
Types of AI of relevance to healthcare
Artificial intelligence is not one technology, but rather a collection
of them. Most of these technologies have immediate relevance
to the healthcare field, but the specific processes and tasks they
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support vary widely. Some particular AI technologies of high
importance to healthcare are defined and described below.
Machine learning – neural networks and deep learning
Machine learning is a statistical technique for fitting models
to data and to ‘learn’ by training models with data. Machine
learning is one of the most common forms of AI; in a 2018
Deloitte survey of 1,100 US managers whose organisations
were already pursuing AI, 63% of companies surveyed were
employing machine learning in their businesses. 1 It is a broad
technique at the core of many approaches to AI and there are
many versions of it.
In healthcare, the most common application of traditional
machine learning is precision medicine – predicting what
treatment protocols are likely to succeed on a patient based on
various patient attributes and the treatment context. 2 The great
majority of machine learning and precision medicine applications
require a training dataset for which the outcome variable (eg onset
of disease) is known; this is called supervised learning.
A more complex form of machine learning is the neural
network – a technology that has been available since the 1960s
has been well established in healthcare research for several
decades 3 and has been used for categorisation applications like
determining whether a patient will acquire a particular disease.
It views problems in terms of inputs, outputs and weights of
variables or ‘features’ that associate inputs with outputs. It has
been likened to the way that neurons process signals, but the
analogy to the brain’s function is relatively weak.
The most complex forms of machine learning involve deep
learning , or neural network models with many levels of features
or variables that predict outcomes. There may be thousands
of hidden features in such models, which are uncovered by the
faster processing of today’s graphics processing units and cloud
architectures. A common application of deep learning in healthcare
is recognition of potentially cancerous lesions in radiology images. 4
Deep learning is increasingly being applied to radiomics, or the
detection of clinically relevant features in imaging data beyond
what can be perceived by the human eye. 5 Both radiomics and deep
learning are most commonly found in oncology-oriented image
analysis. Their combination appears to promise greater accuracy
in diagnosis than the previous generation of automated tools for
image analysis, known as computer-aided detection or CAD.
Deep learning is also increasingly used for speech recognition
and, as such, is a form of natural language processing (NLP),
Authors: A president’s distinguished professor of information
technology and management, Babson College, Wellesley, USA ;
B managing director, Deloitte Consulting, New York, USA
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Artificial intelligence in healthcare
described below. Unlike earlier forms of statistical analysis, each
feature in a deep learning model typically has little meaning to
a human observer. As a result, the explanation of the model’s
outcomes may be very difficult or impossible to interpret.
Natural language processing
Making sense of human language has been a goal of AI
researchers since the 1950s. This field, NLP, includes applications
such as speech recognition, text analysis, translation and other
goals related to language. There are two basic approaches to it:
statistical and semantic NLP. Statistical NLP is based on machine
learning (deep learning neural networks in particular) and has
contributed to a recent increase in accuracy of recognition. It
requires a large ‘corpus’ or body of language from which to learn.
In healthcare, the dominant applications of NLP involve
the creation, understanding and classification of clinical
documentation and published research. NLP systems can analyse
unstructured clinical notes on patients, prepare reports (eg on
radiology examinations), transcribe patient interactions and
conduct conversational AI.
Rule-based expert systems
Expert systems based on collections of ‘if-then’ rules were the
dominant technology for AI in the 1980s and were widely used
commercially in that and later periods. In healthcare, they were
widely employed for ‘clinical decision support’ purposes over
the last couple of decades 5 and are still in wide use today. Many
electronic health record (EHR) providers furnish a set of rules with
their systems today.
Expert systems require human experts and knowledge engineers
to construct a series of rules in a particular knowledge domain.
They work well up to a point and are easy to understand. However,
when the number of rules is large (usually over several thousand)
and the rules begin to conflict with each other, they tend to break
down. Moreover, if the knowledge domain changes, changing the
rules can be difficult and time-consuming. They are slowly being
replaced in healthcare by more approaches based on data and
machine learning algorithms.
Physical robots
Physical robots are well known by this point, given that more than
200,000 industrial robots are installed each year around the
world. They perform pre-defined tasks like lifting, repositioning,
welding or assembling objects in places like factories and
warehouses, and delivering supplies in hospitals. More recently,
robots have become more collaborative with humans and are
more easily trained by moving them through a desired task.
They are also becoming more intelligent, as other AI capabilities
are being embedded in their ‘brains’ (really their operating
systems). Over time, it seems likely that the same improvements
in intelligence that we’ve seen in other areas of AI would be
incorporated into physical robots.
Surgical robots, initially approved in the USA in 2000, provide
‘superpowers’ to surgeons, improving their ability to see, create
precise and minimally invasive incisions, stitch wounds and so
forth. 6 Important decisions are still made by human surgeons,
however. Common surgical procedures using robotic surgery include
gynaecologic surgery, prostate surgery and head and neck surgery.
Robotic process automation
This technology performs structured digital tasks for
administrative purposes, ie those involving information systems,
as if they were a human user following a script or rules. Compared
to other forms of AI they are inexpensive, easy to program and
transparent in their actions. Robotic process automation (RPA)
doesn’t really involve robots – only computer programs on
servers. It relies on a combination of workflow, business rules and
‘presentation layer’ integration with information systems to act
like a semi-intelligent user of the systems. In healthcare, they are
used for repetitive tasks like prior authorisation, updating patient
records or billing. When combined with other technologies like
image recognition, they can be used to extract data from, for
example, faxed images in order to input it into transactional
systems. 7
