writing

ORIGINAL ARTICLE

A population health perspective on artificial intelligence

Maxime Lavigne, BEng, MSc1,2 ; Fatima Mussa, MPH3; Maria I. Creatore, MSc, PhD3,4; Steven J. Hoffman, JD, PhD, LLD3,5,6; and David L. Buckeridge, MD, PhD1,2

Abstract The burgeoning field of Artificial Intelligence (AI) has the potential to profoundly impact the public’s health. Yet, to make the most of this opportunity, decision-makers must understand AI concepts. In this article, we describe approaches and fields within AI and illustrate through examples how they can contribute to informed decisions, with a focus on population health applications. We first introduce core concepts needed to understand modern uses of AI and then describe its sub-fields. Finally, we examine four sub-fields of AI most relevant to population health along with examples of available tools and frameworks. Artificial intelligence is a broad and complex field, but the tools that enable the use of AI techniques are becoming more accessible, less expensive, and easier to use than ever before. Applications of AI have the potential to assist clinicians, health system managers, policy-makers, and public health practitioners in making more precise, and potentially more effective, decisions.

Introduction

The field of Artificial Intelligence (AI) is nearly ubiquitous

with the widespread adoption of products such as automated

translation, face recognition, and semantic searching. Over the

last 50 years, AI-based systems have repeatedly crossed the

boundary of what was thought possible with advances such as

chess, automated translation, and self-driving cars. Current AI-

based systems now demonstrate an unprecedented depth of

reasoning and grasp of our culture.

Although AI has existed for some time, a number of factors

have converged to allow the adoption of AI across a wide

range of fields such as law, political science, policy, and

health.1 As an example of this newly sparked interest, a

prominent textbook on the subject claims that AI is regularly

cited as the “field I would most like to be in” by scientists in

other disciplines.2

In health, the application of AI has led to improvements

in many areas, such as in research on genetics3 and drug

discovery4 and in clinical care through prevention,5 diagnosis,6

therapy planning,7 and optimizing care delivery,8 increasingly

within the context of personalized medicine.9 Part of the appeal

of AI for health applications is the capacity of these methods to

support decisions based on voluminous, heterogenous, and

noisy data and to contribute to sensemaking by providing

new ways to infer knowledge and relationships from data.10

Despite the rapid uptake of AI in research and clinical care, the

adoption has been slower in population health settings, such as

for managing health systems and delivering public health.

To apply AI in population health settings in a manner that is

likely to be effective, decision-makers must have some fluency

in AI concepts, methods, and tools, but this can be difficult to

achieve as the overarching term “AI” is used to mean many

different things. To address this perceived knowledge gap, in

this article, we seek to delineate different approaches and

fields within AI and illustrate through examples how different

sub-fields of AI can contribute to more informed decisions,

with a particular focus on current and potential population

health applications. We begin by introducing the field of AI in

general and presenting the core concepts needed to understand

its contemporary uses. We then proceed to describe the sub-

fields of AI. Finally, we examine more closely the four sub-

fields of AI most relevant to population health and consider the

advantages of each approach along with examples of available

tools and frameworks.

Defining the field of artificial intelligence

Any definition of AI is bound to seem recursive as it is

intrinsically dependent on definitions of intelligence. Two

common definitions are defining AI as “the study of the design

of intelligent agents,”11 and “the art of creating machines

that perform functions that require intelligence when performed

by people.”12

There are two key concepts embodied in these definitions.

First, AI relates to intelligence that is not natural but constructed.

1 Surveillance Lab, McGill Clinical and Health Informatics, McGill University,

Montreal, Quebec, Canada. 2 Department of Epidemiology, Biostatistics, and Occupational Health, McGill

University, Montreal, Quebec, Canada. 3 CIHR Institute of Population and Public Health, Canadian Institutes of Health

Research, Toronto, Ontario, Canada. 4 Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario,

Canada. 5 Global Strategy Lab, York University, Toronto, Canada. 6 Dahdaleh Institute for Global Health Research, Faculty of Health and

Osgoode Hall Law School, York University, Toronto, Ontario, Canada.

Corresponding author:

David L. Buckeridge, Surveillance Lab, McGill Clinical and Health Informatics,

McGill University, Montreal, Quebec, Canada.

E-mail: [email protected]

Healthcare Management Forum 2019, Vol. 32(4) 173-177 ª 2019 The Canadian College of Health Leaders. All rights reserved.

Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/0840470419848428 journals.sagepub.com/home/hmf

Second, we cannot define AI without first agreeing on a func-

tional definition of what intelligence is and how to recognize it.

Defining intelligence has proven to be controversial and

challenging. While the debate continues, most acknowledge

that humans are intelligent beings. This means that, to some

extent, acting intelligently can be defined as acting in a way

that is indistinguishable from how a human would act. This

generally accepted criterion has been named the Turing test

and passing this test was hypothesized to require the skills

listed in Table 1.

In its contemporary use, advances in AI usually refer to

research done in any of these six fields. The preferred term for

research on artificial systems able to perform any intellectual

tasks is “Artificial General Intelligence” or “Strong AI.”

Another useful distinction in AI is between connectionist and

symbolic approaches, which refers to the types of assumptions

made and the kinds of problems addressed.13

Connectionist AI uses data-oriented approaches to derive

decisions based on prior experience. The strengths of these

approaches are that they are tolerant to uncertain or erroneous

data and will work even if there is incomplete or no a priori

knowledge about a situation. The weaknesses, however, are that

the processes used are often opaque and difficult to understand

and they require large amounts of data. Connectionism is akin to

inductive reasoning, or a bottom-up strategy, as it learns by

examples and uses prior experiences when evaluating new

decisions. As an example, given a large dataset with annotated

pictures of footballs and other random objects, a system can

learn to classify whether the subject of a picture is a football

or not. The classification is based solely on how similar or

dissimilar certain features of an image are to images seen

previously.

Symbolic AI uses logic and symbolic approaches to derive

decisions based on an interconnected web of concepts and

relations. The strength of these approaches is that they do not

require data and can make decisions that are context

independent. The reasoning process can also be explained and

communicated. On the other hand, these approaches require the

prior existence of extensive and consistent machine-readable

knowledge. Symbolic approaches traditionally have also

had difficulty dealing with situations that are probabilistic or

continuous in nature. Symbolic AI is similar to deductive

reasoning, or a top-down strategy, since it uses prior knowledge

to infer and reason about new situations. Using the previous

example, symbolic AI systems could be asked to infer whether a

new sport item is a football and to explain what is missing or

what information is not relevant.

It has been proposed that both connectionist and symbolic

AI approaches are needed to achieve human-like cognition.13

The degree to which each approach is used within an

application depends on the task at hand. Different sub-fields of

AI tend to favour one of the two approaches. For example,

knowledge representation tends to be mostly symbolic, while

machine learning is mostly connectionist, and automated

reasoning tends to use both approaches. Below we consider in

greater detail the four sub-fields of AI particularly relevant

to population health: Natural Language Processing (NLP),

knowledge representation, automated reasoning, and machine

learning. In the following sections, we first present a short

summary of each field, followed by a description of recent

advances relevant to population health, and finally some

examples of real-world applications, which interested readers

can use to explore the field further.

Natural language processing

The field of NLP studies human-computer interactions made

through natural human languages, such as English. In other

words, it aims to allow computers to (1) interpret natural

languages, (2) generate responses using natural languages, and

(3) learn new concepts and relations from interactions using

natural language.2 Advances in NLP have led to the development

of frameworks and resources that are now easy to use, readily

available, and often free of charge. One such example is spaCy,

which is described as “industrial-strength natural language

processing” using the Python programming language.14

This field also includes voice recognition, with some

commercial examples of this technology being stable enough

that they are currently sold as digital assistants. Major voice

recognition platforms often offer programmatic interfaces

allowing their use by third parties and researchers.

In health applications, NLP tools are useful for extracting

data from patient medical files, published articles, or even

from social media. Once text is extracted, NLP methods can

also be used to identify the context and meaning of commu-

nication. Examples include case detection15 or phenotyping16

from electronic medical record data, quality assurance,17 and

analyzing social media data to assess vaccine hesitancy,18 and

detect and track emerging health threats.19

Knowledge representation and automated reasoning

We consider these sub-fields together because automated

reasoning often makes extensive use of knowledge representation.

“Automated reasoning” occurs when stored knowledge is used to

answer questions and to draw new conclusions, while “knowledge

representation” refers to the encoding of human knowledge

in a way that is machine-readable, clear, unambiguous, and

consistent.2

Knowledge representation often makes use of formal,

explicit specifications of a shared conceptualization, called

Table 1. Skills required to pass the Turing test2

Natural language processing (NLP) to enable communication with an interlocutor

Machine learning (ML) to adapt to new circumstances and detect or extrapolate patterns

Knowledge representation to store what is known or sensed

Computer vision to perceive objects and understand images

Automated reasoning to use stored information to answer questions and draw conclusions

Robotics to manipulate objects and move about

174 Healthcare Management Forum

software ontologies,20 that describe relations, properties, and

categories of concepts and entities. Ontologies usually have

well-defined domains and can be used for applications such as

creating collaboratively editable representations of domain

knowledge. The languages currently used to encode knowledge

formally in ontologies, however, are limited in their ability to

represent some types of knowledge, such as temporal and

complex relationships, which makes certain problems

intractable.21 The ICD-11 project and “IBM Watson” are two

well-known examples of this field of AI, with the first encoding

knowledge about diseases and the second using reasoning and

deduction in order to win trivia games and more recently to

guide clinical diagnostic and therapeutic decisions. Another

example of greater relevance to population health is the use of

knowledge representation to encode practice guidelines.22 This

application of AI ensures that guidelines can be easily updated

and verified, and any improvements can be automatically

applied to every system that incorporates this knowledge. Such

an approach has the potential to save time and effort in addition

to creating a safer environment in which decisions are made

based on consistent, coordinated, and up-to-date information

that reflects input and updates from all relevant collaborators.

Automated reasoning allows researchers to study decision-

making under constraints and provides a foundation for decision

support systems. The aim of automated reasoning is to develop

methods that can propose logically or probabilistically sound

solutions given a specific question and context. Automated

reasoning is therefore well-positioned to support evidence-based

decision-making. It can, for example, support decision-making

and planning for interventions, which are integral to both clinical

and population health.

An example of the use of automated reasoning could be

a system that chooses the most appropriate intervention

given what is known about a specific patient or population.

Implementation of this type of decision support is critical for

realizing the promise of precision medicine in a clinical

context.23 In a population health context, automated reasoning

plays a similarly central role in supporting the implementation of

learning public health systems, particularly in knowledge

translation24 and precision public health.25

Machine learning

Machine learning has been defined as “the study of data-driven

methods capable of mimicking, understanding and aiding

human and biological information processing tasks. [ . . . ] In

the broadest sense, machine learning and related fields aim to

‘learn something useful’ about the environment within which

the agent operates”.26 There are many similarities between

statistics, commonly used in health applications, and machine

learning. At a general level, both are approaches to learning

from data, although traditional statistics assumes observed data

are generated by a probabilistic data model, while machine

learning assumes data are generated through an unknown

mechanism. This difference between the approaches has

led to some well-known criticisms of traditional statistics; for

example, some have argued that “by being committed to the

almost exclusive use of data models, this commitment has led

to irrelevant theory, questionable conclusions, and has kept

statisticians from working on a large range of interesting

current problems.”27

One of the major differences between the two paradigms is

the underlying objective of the analysis. In machine learning, the

main aim is usually predictive accuracy or generating

predictions that closely match observed data. In contrast, in

traditional statistics, the aim is usually to find the model that best

represents the data. Once a model is defined and parameters are

estimated, any conclusions made are conditional on how

appropriately the model represents the true underlying process.

As increasingly more complex data models are proposed and

used, proponents of machine learning suggest that automated

approaches to model-building should be considered.27 However,

by moving away from parametric models, there is a chance to

lose sight of the original purpose of the inquiry. This situation

can be particularly problematic in fields such as epidemiology,

where questions often relate to the data generation mechanism

itself (ie, identifying causal mechanisms) and are less frequently

about how closely an action can be replicated (ie, predicting

likely outcomes). However, some machine learning researchers

have suggested that traditional statistical methods may also not

be that useful for answering causal questions. The reason

being that the natural mechanisms of interest are often either

unobserved or unobservable and therefore estimates of their

properties may be artificial products of the assumptions made in

specifying the data model.27

An important classification of machine learning approaches

is based on the type of learning used. The three major types of

learning are supervised, unsupervised, and reinforcement

learning.2 In supervised learning, agents are provided

with input-output pairs that have been correctly identified

beforehand. Here, agents learn to replicate or mimic the correct

answer as closely as they can. Supervised learning is used in

tasks such as prediction and classification. In unsupervised

learning, however, agents learn patterns and identify relations

from data without explicit labelling of the outcome. This

approach is useful for applications such as clustering, pattern

recognition, or the discovery of latent factors. Finally, in

reinforcement learning, agents learn from positive or negative

interactions with their environment and themselves. The kinds

of tasks being achieved will depend on the reinforcement

provided.

Both statistical learning and machine learning require large

datasets of good quality. Any model that learns from data will

be improved by more up-to-date, diverse, expressive, and

larger amounts of data. Similarly, poor quality data, such

as data sampled in a biased manner, can result in models

that make erroneous or biased predictions. Consequently,

methodologies aimed at improving the process of gathering and

analyzing data will have a strong impact on the performance of

machine learning algorithms. This relationship between

machine learning and “Big Data” is why the two topics are

often discussed in the same context. Many of the recent

Lavigne, Mussa, Creatore, Hoffman and Buckeridge 175

advances in AI applied to health are from the use of machine

learning, and in particular the use of deep neural networks for

supervised learning. For example, these types of AI-based

approaches have been shown to classify the results of

radiological imaging more accurately than human experts.28

This level of performance has yet to be demonstrated in

supporting high-level decision-making in population health

settings, possibly because the data tend to be more complex in

structure. For example, because concepts are often measured

indirectly, it can be challenging to ensure the data represent the

concept in an unbiased manner, and correct interpretation

requires a broad set of domain and contextual knowledge.

Some promising applications have been reported, however, in

areas such as predicting population characteristics from remote

sensing data29 and predicting environmental exposures from

historical satellite/street-level images.30

Conclusion

In this article, we described and attempted to delineate different

approaches and fields within AI to help cut through some of

the hype and jargon and to assist readers in identifying

opportunities for AI in population health. We also considered

in greater detail the four sub-fields of AI that we believe have

the greatest potential for population health applications.

A central message of this article is that AI is a broad field

with many sub-fields, even though AI is often used to refer to

one sub-field or approach, such as knowledge representation or

machine learning. The sub-field of machine learning in

particular is now being applied to many clinical problems, but

machine learning has been applied less frequently to population

health or public health problems. Machine learning approaches

are similar and complementary to traditional statistical

approaches, and both have the potential to play an important

role in using “Big Data” to understand and predict healthcare

and population health outcomes. However, particularly when

applying these approaches to decision-making or predictions at

a population level, attention must be paid to the potential for

these approaches to produce health inequities, either through the

use of biased data or through uneven access to the technology.

Predictions and models based on non-representative or biased

data can propagate underlying biases and exacerbate health

inequities at a population level if sufficient care is not taken to

mitigate these issues.

As a practical example to illustrate the current potential of

AI, it would be possible to develop an automated system that

uses NLP to read medical charts to detect cases of a disease of

public health importance from plain text. These new cases

could be linked to current knowledge using the ICD-11 disease

ontology. Once resolved to known conditions, the cases could

be analysed by machine learning methods in a surveillance

system to detect outliers or clusters. Unusual patterns could

then be presented to practitioners, drawing on other knowledge

to highlight the potential magnitude of a public health problem

and to predict the potential growth in cases. Finally, an expert

system could then integrate the information about the cases

with computable knowledge about potential interventions and

use automated reasoning to propose effective public health

interventions.

While predicting the future is always challenging, it appears

that the contributions of AI to clinical and population health

will continue to expand. The tools that enable the use of AI

techniques are becoming more accessible, less expensive, and

easier to use than ever before. Applications of AI have the

potential to assist clinicians, health system managers,

public health practitioners, and policy-makers in making more

precise, and potentially more effective, decisions. Efforts to do

so, however, are more likely to be successful if they are

developed through collaborations across sectors and if they

make available interoperable AI-based applications that use

domain knowledge and learn from experience.

Declaration of conflicting interests

M.L. and D.L.B. work for McGill University. F.M., M.I.C., and S.J.H.

work for the Canadian Institutes of Health Research (CIHR), the

Government of Canada’s national health research investment agency.

The views expressed in this article are those of the authors and do not

necessarily reflect those of CIHR or the Government of Canada.

ORCID iD

Maxime Lavigne https://orcid.org/0000-0002-6401-6381

David L. Buckeridge https://orcid.org/0000-0003-1817-5047

References

1. Stone P, Brooks R, Brynjolfsson E, et al. Artificial Intelligence

and Life in 2030. One Hundred Year Study on Artificial Intelli-

gence: Report of the 2015-2016 Study Panel. Stanford University,

Stanford, CA; September 2016. http://ai100.stanford.edu/2016-

report

2. Russell SJ, Norvig P. Artificial Intelligence: A Modern Approach.

Harlow, Malaysia: Pearson Education Limited; 2016.

3. Libbrecht MW, Noble WS. Machine learning applications in

genetics and genomics. Nat Rev Genet. 2015;16(6):321-332.

4. Fleming N. How artificial intelligence is changing drug

discovery. Nature. 2018;557(7707):S55-S57.

5. Liu Y, Kohlberger T, Norouzi M, et al. Artificial intelligence—

based breast cancer nodal metastasis detection: insights into the

black box for pathologists. Arch Pathol Lab Med. 2018.

6. Gulshan V, Peng L, Coram M, et al. Development and validation

of a deep learning algorithm for detection of diabetic retinopathy

in retinal fundus photographs. JAMA. 2016;316(22):2402-2410.

7. Babier A, Boutilier JJ, McNiven AL, Chan TCY. Knowledge-

based automated planning for oropharyngeal cancer. Med Phys.

2018;45(7):2875-2883.

8. Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big

data in health care: using analytics to identify and manage high-

risk and high-cost patients. Health Aff. 2014;33(7):1123-1131.

9. Fröhlich H, Balling R, Beerenwinkel N, et al. From hype to

reality: data science enabling personalized medicine. BMC Med.

2018;16(1):150.

176 Healthcare Management Forum

10. Bellazzi R, Zupan B. Predictive data mining in clinical medicine:

current issues and guidelines. Int J Med Inform. 2008;77(2):

81-97.

11. Poole DL, Mackworth AK, Goebel R. Computational

Intelligence: A Logical Approach . New York, NY: Oxford

University Press New York; 1998. Vol. 1.

12. Kurzweil R. The Age of Intelligent Machines. Cambridge, MA:

MIT Press; 1990.

13. Minsky ML. Logical versus analogical or symbolic versus

connectionist or neat versus scruffy. AI Magazine. 1991;12(2):34.

14. Honnibal M, Montani I. spaCy 2: Natural language understanding

with Bloom embeddings, convolutional neural networks and

incremental parsing. 2017.

15. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detec-

tion—harnessing the web for public health surveillance. N Eng J

Med. 2009;360(21):2153-2157.

16. Richesson RL, Sun J, Pathak J, Kho AN, Denny JC. Clinical

phenotyping in selected national networks: demonstrating the

need for high-throughput, portable, and computational methods.

Artif Intell Med. 2016;71:57-61.

17. Freifeld CC, Brownstein JS, Menone CM, et al. Digital drug

safety surveillance: monitoring pharmaceutical products in

twitter. Drug Saf. 2014;37(5):343-350.

18. Salathé M, Khandelwal S. Assessing vaccination sentiments with

on-line social media: implications for infectious disease dynamics

and control. PLoS Comput Biol. 2011;7(10):e1002199.

19. Mawudeku A, Blench M. Global public health intelligence network

(GPHIN). Paper presented at: 7th Conference of the Association for

Machine Translation in the Americas, Cambridge, Massachusetts;

August 8-12, 2006;8-12.

20. Staab S, Studer R. Handbook on Ontologies. Berlin, Heidelberg:

Springer Science & Business Media; 2010.

21. Lukasiewicz T, Straccia U. Managing uncertainty and vagueness

in description logics for the semantic web. Web Semant. 2008;

6(4):291-308.

22. Peleg M. Computer-interpretable clinical guidelines: a

methodological review. J Biomed Inform. 2013;46(4):744-763.

23. Sittig DF, Wright A, Osheroff JA, et al. Grand challenges in

clinical decision support. J Biomed Inform. 2008;41(2):387-392.

24. Riaño D, Real F, López-Vallverdú JA, et al. An ontology-based

personalization of healthcare knowledge to support clinical

decisions for chronically ill patients. J Biomed Inform. 2012;

45(3):429-446.

25. Haendel MA, Chute CG, Robinson PN. Classification, ontology,

and precision medicine. New Eng J Med. 2018;379:1452-1462.

26. Barber D. Bayesian Reasoning and Machine Learning. New

York, NY: Cambridge University Press; 2012.

27. Breiman L. Statistical modeling: the two cultures (with comments

and a rejoinder by the author). Stat Sci. 2001;16(3):199-231.

28. Yu KH, Zhang C, Berry GJ, et al. Predicting non-small cell lung

cancer prognosis by fully automated microscopic pathology

image features. Nat Commun. 2016;7:12474.

29. Gebru T, Krause J, Wang Y, et al. Using deep learning and

Google Street View to estimate the demographic makeup of

neighborhoods across the United States. Proc Nat Acad Sci.

2017;114(50):13108-13113.

30. Weichenthal S, Hatzopoulou M, Brauer M. A picture tells a

thousand . . . exposures: opportunities and challenges of deep

learning image analyses in exposure science and environmental

epidemiology. Environ Int. 2019;122:3-10.

Lavigne, Mussa, Creatore, Hoffman and Buckeridge 177

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/FlattenerPreset << /ClipComplexRegions true /ConvertStrokesToOutlines false /ConvertTextToOutlines false /GradientResolution 300 /LineArtTextResolution 1200 /PresetName ([High Resolution]) /PresetSelector /HighResolution /RasterVectorBalance 1 >> /FormElements true /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles true /MarksOffset 9 /MarksWeight 0.125000 /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /DocumentCMYK /PageMarksFile /RomanDefault /PreserveEditing true /UntaggedCMYKHandling /UseDocumentProfile /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ] /SyntheticBoldness 1.000000 >> setdistillerparams << /HWResolution [288 288] /PageSize [612.000 792.000] >> setpagedevice