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 ,Application of Artificial Intelligence in Healthcare

electronic health record systems


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





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

Application of Artificial Intelligence in Healthcare