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https://doi.org/10.1177/2329488418819139
International Journal of
Business Communication
2022, Vol. 59(1) 126 –147
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DOI: 10.1177/2329488418819139
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Article
Artificial Intelligence in
Business Communication:
A Snapshot
Jefrey Naidoo1 and Ronald E. Dulek1
Abstract
Despite artificial intelligence’s far-reaching influence in the
financial reporting and
other business domains, there is a surprising dearth of
accessible descriptions about
the assumptions underlying the software’s development along
with an absence of
empirical evidence assessing the viability and usefulness of this
communication tool.
With these observations in mind, the purposes of this study are

to explain how
automated text summarization applications work from an
overarching, semitechnical,
modestly theoretical perspective and, using ROUGE-1 (Recall-
Oriented Understudy
for Gisting Evaluation–1) evaluation metrics, assess how
effective the summarization
software is when summarizing complex business reports. The
results of this study
show that the extraction-based summarization system produced
moderately
satisfactory results in terms of extracting relevant instances of
the text from the
business reports. Much work still needs to be accomplished in
the area of precision
and recall in extraction-based systems before the software can
match a human’s
ability to capture the gist of a body of text.
Keywords
ROUGE-1, automatic text summarization, artificial intelligence,
company annual reports
The rapid advances made in machine learning over the past few
decades have paved the
way for a prolific rise in a new generation of sophisticated
artificial intelligence (AI)
systems that can perform tasks autonomously. AI is arguably the
most important tech-
nology innovation of our era (Brynjolfsson, Rock, & Syverson,
2017); its transforma-
tive impact has been felt in almost every societal domain.
Intelligence communities are
leveraging AI across their portfolios to strengthen national
security, reduce biological

1University of Alabama, Tuscaloosa, AL, USA
Corresponding Author:
Jefrey Naidoo, University of Alabama, Stadium Drive,
Tuscaloosa, AL 35487-0001, USA.
Email: [email protected]
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Journal of Business CommunicationNaidoo and Dulek
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Naidoo and Dulek 127
warfare, and mitigate cyber threats (Allen & Chan, 2017); legal
firms are employing AI
to enhance legal informatics, predict litigation, and measure
workflows in real time
(Sobowale, 2016); health care entities are utilizing AI to
perform clinical diagnostics on
medical images at levels equal to those of experienced
clinicians (HealthIT, 2017); the
airline industry is engaging AI to reduce “human-steered” flight
time to only 7 minutes
of the total flight time (Narula, 2018); and, finally, social media
platforms are deploying
AI to generate a more personalized and interactive user
experience.
AI’s pervasive impact has extended into the business
environment as well. By pro-
viding tools that automate redundant tasks, identify patterns
within data, and uncover

valuable insights, AI has helped corporations automate routine
processes and improve
overall process performance. These improvements have taken
the form of enhanced
compliance, security and risk management; increased gains in
productivity and market
share; and improved employee retention (Jha, 2018). A recent
global survey of 1,600
business decision makers found that 76% of the respondents
believed that AI is funda-
mental to future business success, while 64% believed that their
organization’s future
growth is dependent on AI adoption. The survey also found that
companies expect AI
to contribute an average revenue increase of 39% by 2020
(Infosys, 2018).
Its value proposition seemingly endless, AI has entered the
domain of business
communication in a number of ways, with perhaps the most
pronounced being auto-
matic text summarization of corporate disclosures (Cardinaels,
Hollander, & White,
2017). Large financial institutions, such as Citicorp and Bank of
America; regulators,
such as the Securities and Exchange Commission; and investors
are key beneficiaries
of this type of summarization (Barth, 2015). The first two
entities see similar effi-
ciency benefits from summarization software because
disclosures have, over the years,
become fairly protracted and include a substantial amount of
redundancy (Dyer, Lang,
& Stice-Lawrence, 2017). The third group, investors, including
hedge fund investors,
employs AI engines to analyze macroeconomic data, assess

market fundamentals, and
analyze corporate financial disclosures, each with the intention
of making more accu-
rate market predictions and executing more successful stock
trades (Metz, 2016).
Yet despite AI’s far-reaching influence in the financial
reporting and other business
domains, there is a surprising dearth of accessible descriptions
about the assumptions
underlying the software’s development along with an absence of
empirical evidence
assessing the viability and usefulness of this communication
tool. The lack of the for-
mer means that we need a kind of pretheory about
summarization software; the lack of
the latter means that we have yet to determine how effective
automatic text summari-
zation software is as a business communication tool.
With the above observations in mind, the purposes of this study
are threefold:
1. To explain how automated text summarization applications
work from an
overarching, semitechnical, modestly theoretical perspective
2. To study how effective the summarization software is when
summarizing com-
plex business reports
3. To explore variances between outputs produced by human
authors and artifi-
cial intelligence for the selected data genre

128 International Journal of Business Communication 59(1)
To measure the effectiveness of summarization software, we
first created manual
(human-authored) summaries of the Letter to Shareholders in 10
Fortune 500 company
annual reports that were published in 2018. Next, we used an
automated extraction-based
text summarization application, Resoomer, to produce machine-
generated summaries of
the same documents. We then used ROUGE-1 (Recall-Oriented
Understudy for Gisting
Evaluation–1), a highly regarded and widely employed set of
metrics for evaluating auto-
matic summarization, to conduct our assessment of efficacy and
determine variances
between the outputs produced within the respective summary
categories.
This study makes several contributions to the body of literature
in business com-
munication and to the business field at large. First, as the
software continues to become
more and more effective, the manner in which business
summaries are written is going
to change dramatically. It therefore seems wise for the field’s
researchers and practi-
tioners to familiarize themselves with how this family of
application software works
as well as to determine where we are in terms of the software’s
efficacy.
Second, this is the first evaluative study of automatic text
summarization conducted
on this specific instrument of strategic business communication.

We considered a broad
range of data corpora to serve as potential datasets for this
evaluation. We concluded that
Letters to Shareholders worked well for this study because they
provide an important
business communication bridge between the voluntary and
mandatory information dis-
closures of public companies (Williams, 2008). Additionally,
the subject matter of these
letters reaches across a variety of business enterprises and
disciplines. Hence, we decided
that these letters provide an objective way to evaluate the
effectiveness of summarization
software as a business communication tool, with the letters
functioning as independent
variables on which to test the summarization software’s
effectiveness. We are not evalu-
ating the design, effectiveness, or even the strategic approach of
the Letters themselves.
Finally, the study calls attention to evolutionary developments
and practices in the busi-
ness communication space. By condensing large business
documents into short, informa-
tive summaries, automatic text summarization is expediting
information communication in
business environments, thereby affecting what the organization
knows. Additionally, it
likely affects organizational decision making as well other
downstream processes such as
information searches and report generation (Paulus, Xiong, &
Socher, 2017).
In the following sections of this article, we provide an extensive
exposition of how
the software works from an overarching, semitechnical

perspective. We then describe
the selection, extraction, and processing of the dataset and
conclude with an analysis
and discussion of our results and findings.
Overview of Automated Text Summarization and
Evaluation
While appearing simple to do on the surface, the act of
summarizing text is actually a
highly complex task that involves summarization of source
codes based on software
reflection and lexical source model extraction (Murphy &
Notkin, 1996). Proof of its
complexity is found in the fact that developers have been
working for decades to make
this software viable and to advance its efficacy.
Automated text summarization systems endeavor to produce a
concise summary of
the source or reference text while retaining its fundamental
essence and overall mean-
ing. The system’s goal is to generate a summary of the source or
reference text that is
equivalent to a summary generated by a human (Brownlee,
2017). A three-phase pro-
cess generally characterizes these systems:
1. An analysis of the source text
2. The determination of its salient points
3. A synthesis of an appropriate output (Alonso, Castellon,
Fuentes, Climent, &

Horacio Rodriquez, 2003)
Previous studies (e.g., Smith, Patmos, & Pitts, 2018) have found
that much work still
needs to be accomplished in the area of precision and recall in
extraction-based systems
before the software can match a human’s ability to capture the
gist of a body of text.
Seminal work in automatic text summarization began in the
1950s, with the first
sentence extraction algorithm being developed in 1958
(Steinberger & Jezek, 2009).
The algorithm used term frequencies to measure the relevance
of the sentence.
Understandably, the methods developed during that era were
fairly rudimentary (Hovy
& Lin, 1998). Since then, a large number of techniques and
approaches have been
developed. Interestingly, the large volumes of information
created on the web have
triggered much of this development (Nenkova & McKeown,
2011; Shams, Hashem,
Hossain, Akter, & Gope, 2010). Bhargava, Sharma, and Sharma
(2016) posit that text
summarization tools have now become a necessity to navigate
the information on the
web because they help eliminate dispensable or superfluous
content. Torres-Moreno
(2014) asserts that automatic text summarization reduces
reading time, expedites
research by making the selection process of documents easier,
employs algorithms that
are less biased than human summarizers, improves the
effectiveness of indexing, and

enables commercial abstraction services to increase the number
of texts they are able
to process. All in all, high praise for the software.
Extraction-Based Text Summarization
Automatic text summarization systems utilize different
summarization techniques to
condense source text. The vast majority of today’s
summarization algorithms employ
what is referred to as an extraction-based approach (Saggion &
Poibeau, 2012). The
flexibility and greater general applicability of the extraction-
based approach make it
the preferred approach for most business summaries (Liu & Liu,
2009).
Extraction-based techniques involve the analysis of text features
at the sentence level,
discourse level, or corpus level to locate salient text units that
are extracted, with mini-
mal or no modification, to formulate a summary of the text (Liu
& Liu, 2009). Stated
more simply, in extraction-based text summarization, relevant
phrases and sentences are
selected from the source document and rearranged into a new
summary sequence (Paulus
et al., 2017). The summary, then, is essentially a subset of the
sentences in the original
source or reference text (Allahyari et al., 2017).
Salient text units are identified by evaluating their linguistic

and statistical rele-
vance or by matching phrasal patterns (Hahn & Mani, 2000).
Statistical relevance is
based on the frequency of certain elements in the text, such as
words or terms, while
linguistic relevance is determined from a simplified
argumentative structure of the
text (Neto, Freitas, & Kaestner, 2002). These parameters serve
as inputs to a combi-
nation function with modifiable weights to derive a total score
for each text unit. Text
units with a concentration of high-score words are often likely
contenders for extrac-
tion (Liu & Liu, 2009). Extraction-based summarization, then,
is essentially con-
cerned with evaluating the salience or the indicative power of
each sentence in a
given document (Shams et al., 2010). Figure 1 maps out the
process flow for extrac-
tion-based systems.
Evaluation of Text Summarization Using ROUGE-1 Metrics
Intrinsic evaluations of text summarization outputs
conventionally involved manual
human assessments of the quality and utility of a given
summary. Rubrics based on
coherence, conciseness, grammaticality, readability, and content
provided the guid-
ance for these human assessments (Mani, 2001). Given the
potential for bias, and the
time-consuming nature of the process, this practice gradually
evolved into automatic
comparisons of the summaries with human-authored gold
standards thus minimizing
the need for human involvement (Nenkova, 2006).

Today, various summarization evaluation systems and methods
employing sophisti-
cated algorithms may be used to compare human-authored
summaries with machine-
generated summaries. One such method, ROUGE, the most
widely used metric for
automatic evaluation (Allahyari, 2017), was found to produce
evaluation rankings that
correlate reasonably with human rankings (Lin, 2004). It
leverages numerous measures
to automatically determine the quality of a computer-generated
summary. The mea-
sures include, but are not limited to, a count of variables such
as word sequences and
Source
Text
Term Frequency Counts
Pattern Matching Ops.
Presence of Specific Terms
Sentence Location
Statistical
Metrics
Weight
Selection
Extraction
Analysis Synthesis

Lexical
Metrics
Figure 1. Process flow for extraction-based systems.
word pairs between the computer-generated summary and the
reference summary cre-
ated by humans (Lytras, Aljohani, Damiani, & Chui, 2018).
In this study, we used ROUGE-1 evaluation metrics that
measure the overlap of
words (unigrams) between the machine-generated and reference
summaries and pro-
vide three measures of quality.
Recall. Also known as sensitivity, this is the measure of the
fraction of relevant
instances that have been retrieved over the total amount of
relevant instances. Stated
more simply, it is the computation of the number of overlapping
words between the
machine-generated summary and the reference summary (i.e.,
number of overlapping
words/total number of words in reference summary).
Precision. Also called positive predictive value, this is the
measure of the fraction of
relevant instances among the retrieved instances. In other
words, it is the computation
of how much of the machine-generated summary is actually
relevant or essential (i.e.,

number of overlapping words/total words in machine-generated
summary).
F1-Score. This score is a weighted average of the precision and
recall. A score of 1
suggests perfect precision and recall, while a score of 0
indicates the opposite. This
measure is regularly employed in the field of information
retrieval to provide a quan-
tifiable assessment of performance (Beitzel, 2006).
Each of the above standards is arguably more precise than
subjective human calcu-
lations of coherence and conciseness.
Method
Data Corpus
Our data corpus was composed of Letters to the Shareholders of
a subset of corpora-
tions listed on the Fortune 100 list for 2017. Letters to the
Shareholders are voluntary
inclusions in the annual report, usually appearing as an
introduction. Considered an
important piece of information (Vozzo, 2016), these documents
provide useful insight
into the quality of leadership at the corporation and
management’s commitment to
creating meaningful long-term value for shareholders (Heyman,
2010).
Wielding much influence in investment transactions, Letters to
Shareholders are
integral to an investor’s due diligence process. They are read
with much interest by

professional investors, analysts, and other stakeholders
(Heyman, 2010). Additionally,
these letters often supplement the overall effort to frame the
annual report’s informa-
tion through narrative and graphical strategies (Laskin, 2018;
Penrose, 2008). The
ability to effectively and accurately summarize the most salient
content from these
letters may, therefore, offer significant value to its readership.
To ensure that we obtained a meaningful understanding of the
effectiveness of auto-
mated text summarization applications, we elected to focus our
investigation on a small,
purposive sample of 10 Fortune 100 corporations. To this end,
we selected the top 10
corporations listed in the Fortune 100 list for 2017. Two of the
top 10 corporations,
Apple and United Health, however, did not include a Letter to
the Shareholders in their
respective annual reports. We sought to replace them with
letters from the corporations
listed in 11th and 12th place, respectively. However, the annual
report for the corpora-
tion listed in 11th place, AmerisourceBergen, was unavailable
at the time of the study.
Ultimately, Letters to the Shareholders from Amazon, listed in
12th place, and General
Electric, listed in 13th place, were included in the dataset in
lieu of Letters to the
Shareholders from Apple and United Health.

Corpus Extraction and Preparation
We located the Letters to the Shareholders on the respective
corporate websites and
reformatted the PDF files into text files. We conducted a
manual inspection of each
Letter to the Shareholders and removed all redundant graphics
and images. In addition
to harmonizing the data corpus, this exercise ensured that the
datatype was exclusively
text based.
Procedure
There are generally two ways to assess the quality of automatic
text summarization
output. The first method, referred to as extrinsic evaluation,
assesses the usefulness of
the output summary in a task-based setting. Here, the summary
is used to support the
completion of a specific task. Its usefulness is determined by
measuring established
metrics for task completion efficiency (Hirschberg, McKeown,
Passonneau, Elson, &
Nenkova, 2005). The second method, referred to as intrinsic
evaluation, is conducted
by “by soliciting human judgments on the goodness and utility
of a given summary, or
by a comparison of the summary with a human-authored gold
standard” (Nenkova &
McKeown, 2011, p. 199).
For this study, we employed the intrinsic evaluation method.
We first compared the
machine-generated text summary with a human-authored

summary as prescribed in
the literature to assess the “goodness” of the machine-generated
summary. To this end,
human-generated summaries and machine-generated summaries
were produced for
each Letter to the Shareholders at predetermined levels of
reduction. Each machine-
generated summary was then assessed by the summarization
evaluation system,
ROUGE-1, using the human-authored summary as the source or
reference text. This
method is consistent with standard practice in automated text
summarization evalua-
tion as noted by Nenkova and McKeown (2011).
Subsequently, in an effort to explore potential variances
between the outputs pro-
duced by human authors and artificial intelligence for the
selected data genre, we
conducted a second-phase evaluation in which we employed
ROUGE-1 metrics to
evaluate the “goodness” of
•• the human-authored summary for each company using the
respective Letter to
the Shareholders for that company as the reference summary
and
•• the machine-generated summary for each company using the
respective Letter
to the Shareholders for that company as the reference summary

In doing so, we aimed to assess the extent of the variance, if
any, in the recall, preci-
sion, and F-measures between the two summary classes (i.e.,
human-authored and
machine-generated). We hypothesized that comparable scores
between the two summary
classes in each of those corresponding measures would likely
indicate a similarity in the
quality and utility of the summaries, while widely disparate
scores would suggest the
alternative. Ultimately, either outcome would provide a broader
commentary on the effec-
tiveness of the summarization software when summarizing
complex business reports.
In summary, then, we employed ROUGE-1 metrics to evaluate
•• the machine-generated summary against the human-authored
summary,
•• the human-generated summary against the original Letter to
the Shareholders, and
•• the machine-generated summary against the original Letter to
the Shareholders.
Following is a more detailed description of each of these
processes.
Formulation of Datasets. To reiterate, two distinct categories of
summaries were pro-
duced for each Letter to the Shareholders (i.e., human-authored
summaries and
machine-generated summaries). To facilitate a more robust
investigation, two sum-
maries were produced within each category, each differentiated
by the total word
count. The first summary was capped at 10% of the word count

of the original Letter
to the Shareholders; the second summary was capped at 20%.
Thus, the dataset for
each company comprised the following data:
•• A human-authored summary capped at 10% of the word count
of the original
Letter to the Shareholders
•• A human-authored summary capped at 20% of the word count
of the original
•• A machine-generated summary capped at 10% of the word
count of the original
•• A machine-generated summary capped at 20% of the word
These summaries served as the input data for the study. A more
detailed description of
the process to create the data follows.
Human-authored summaries. A writer trained and experienced
in writing business
summaries generated summaries at both summarization levels
(i.e., 10% and 20%) of
all the documents in the data corpus. The word count was
validated using MSWord’s
word count feature. To mitigate bias, this writer was not
involved in processing the
Letter to the Shareholders through the automated text
summarization application.

Machine-generated summaries. Simultaneously, the text files
were processed indi-
vidually through the online automated text summarization
application. Resoomer
was selected because of its current popularity as a text
summarization tool and its
demonstrated superiority over other online text summarization
applications in terms
of functionality, ease of use, and accuracy (Hobler, 2017;
Nyzam, Gatto, & Bossard,
2017). An advanced feature of this application is the ability to
set the summarization
to a desired level of word count reduction. Accordingly, the
summarization level
was set first to 10% and then to 20% of the word count of the
original Letter to the
Shareholders. The resulting machine-generated summaries were
saved as MSWord
documents.
Evaluation of Summaries. The human-authored and machine-
generated summary for each
corporation was then processed in the ROUGE-1 notepad
interface, and the evaluation run
was executed. The resulting scores were captured in an Excel
spreadsheet and evaluated.
Figure 2 provides a visual representation of the process flow.
Data Corpus
Letters to the

Shareholders (LTS) in
Published Format
Step 1: Corpus
Preparation
Removal of Images and
Infographics
Harmonized Data
Corpus
LTS in Text Format
Step 2: Formulation of
Datasets
Data category 1: Human-
authored Summaries
Produced by Researcher
Word-counts of 10% and 20% of
original LTS, respectively
Data category 2: Machine-
generated Summaries
Produced by Text Summarization
application (Resoomer)
Word-counts of 10% and 20% of
original LTS, respectively
Step 3: Evaluation of

Datasets using
ROUGE-1 metrics
i) Evaluation of
Machine-generated
summary with Human-
authored summary as
reference
ii) Evaluation of Human-
authored summary with
original LTS as reference
iv) Evaluation of
Machine-generated
summary with original
LTS as reference
Step 4: Evaluation of variances in
human and machine-generated output using boxplots
Figure 2. Method flowchart.
Results
Example of Outputs

Following are examples of the output summaries for one Letter
to the Shareholders
from the data corpus.
Corporation: ExxonMobil
Original Word Count: 510
Human-Authored Summaries. Reduction: 10% of original word
count (51 words)
Winning involves capturing value, maintaining a technological
edge, and operating safely and responsibly.
ExxonMobil’s financial future looks promising. It invests in
growth projects and integrates in ways
competitors cannot. Innovation occurs through technical
exploration and the development of environ-
mentally friendly products with higher financial returns.
ExxonMobil is an industry leader.
Reduction: 20% of original word count (102 words)
Winning involves capturing value, maintaining a technological
edge, and operating safely and
responsibly.
ExxonMobil is an industry leader. Its financial future looks
promising. We invest in high-value growth
projects and integrate in ways competitors cannot. We are
adding new low-cost supplies of LNG. We are
ramping up unconventional production. We use proprietary
technology to produce higher value products.
Innovation occurs through technical exploration and the
development of environmentally friendly prod-
ucts with higher financial returns. Our technology investments
build a foundation for the future—creat-
ing long-term value for society. We lead in the discovery of

scalable technologies.
ExxonMobil is an industry leader.
Machine-Generated Summaries. Reduction: 10% of original
word count (46 words)
Winning in today’s energy business takes a cost to the whole
commodity cycle. In our Downstream,
we’re using our proprietary technology to produce higher value
products. Innovative products pioneered
in our Chemical business are enabling a growing global middle
class to enjoy a higher quality of life.
Reduction: 20% of original word count (98 words)
Winning in today’s energy business takes a cost to the whole
commodity cycle. A company is able to
capture value across the supply chain. In our Downstream,
we’re using our proprietary technology to
produce higher value products. And in our Chemical business,
we are investing in capacity and manu-
facturing to meet the needs of growing economies around the
world. ExxonMobil is investing for high-
value growth.
Innovative products pioneered in our Chemical business are
enabling a growing global middle class to
enjoy a higher quality of life. Our innovation is delivering value
to our customers, our communities, and
you, our shareholders.
ROUGE-1 Scores for Precision, Recall, and F-Measures
In Tables 1 to 6, we report ROUGE-1 scores when specific
summaries are evaluated
against a reference summary. As mentioned earlier, the

reference summary is deemed
to be the ideal or standard document against which the ROUGE-
1 algorithm evaluates
other summaries for precision and recall.
Evaluation of Machine-Generated Summaries Against Human-
Authored Summaries. To
maintain consistency with the standard protocol defined in the
literature for conducting
evaluations of automated text summarization outputs, we
designated the human-
authored summaries as the reference summaries. We then
evaluated the machine-gen-
erated summary for each company against the reference
summary for that company.
Table 1 shows ROUGE-1 scores (average recall, average
precision, and average
F1-score) for input documents summarized to 10% of the word
Letter to the Shareholders. For illustrative purposes, the average
recall score of 0.21
for Walmart in Table 1 implies that 21% of the words
(unigrams) in the machine-gen-
erated summary are also present in the human-authored
summary for this company.
The corresponding precision score of 0.20 implies that only
20% of the overlapping
words in the machine-generated summary were actually
relevant. The F-measure of
0.21, the weighted average of the recall and precision,

essentially quantifies the per-
formance efficiency of the automatic text summarization tool.
Table 2 shows ROUGE-1 scores for input documents
summarized to 20% of the
word count of the original Letter to the Shareholders.
Evaluation of Human-Authored Summaries Against the Original
Letters to the Shareholders. In
this instance, we designated the original Letters to the
Shareholders as the standard/ ideal/
reference summaries. We evaluated the human-authored
summary for each company
against the reference summary (Letters to the Shareholders) for
that company. Our goal in
doing so was to assess the integrity of the human-authored
summaries. Table 3 shows
ROUGE-1 scores for human-authored summaries compiled at
10% of the word count of
the original Letter to the Shareholders. In this case, the average
recall score of 0.05 for
Table 1. Evaluation of Machine-Generated Summaries Using
Human-Authored Summaries
as Reference (10% Summarization Level).a
Corporation Average recall Average precision Average F1-score
Walmart 0.21 0.20 0.21
Exxon Mobil 0.13 0.11 0.12
Berkshire Hathaway 0.25 0.27 0.26
McKesson 0.22 0.23 0.23
CVS Health 0.27 0.22 0.24
Amazon.com 0.21 0.22 0.22
AT&T 0.23 0.26 0.25
General Motors 0.26 0.27 0.26

Ford 0.15 0.20 0.17
GE 0.27 0.19 0.22
Mean 0.22 0.22 0.22
aRounded to two decimal places.
Walmart in Table 3 implies that there is a 5% overlap in words
(unigrams) between the
human-authored summary and the original Letter to the
Shareholders for this company.
The corresponding precision score of 0.49 implies that almost
50% of the overlapping
words in the human-authored summary were actually relevant.
The F-measure of 0.09
quantifies the performance efficiency of the automatic text
summarization tool.
Table 4 shows ROUGE-1 scores for human-authored summaries
condensed to 20%
of the word count of the original Letter to the Shareholders.
Comparison of Machine-Generated Summaries With Original
Letters to the Sharehold-
ers. Here, we once again designated the original Letters to the
Shareholders as the
Table 3. Evaluation of Human-Authored Summaries Using
Original Letter to the
Shareholders as Reference (10% Summarization Level).a

Walmart 0.05 0.49 0.09
Exxon Mobil 0.03 0.31 0.06
McKesson 0.06 0.49 0.10
CVS Health 0.05 0.48 0.09
Amazon.com 0.05 0.47 0.09
AT&T 0.05 0.48 0.10
Ford 0.07 0.48 0.12
General Electric 0.05 0.48 0.10
Mean 0.05 0.46 0.09
Human-Authored Summaries
as Reference (20% Summarization Level).a
Walmart 0.26 0.26 0.26
Exxon Mobil 0.20 0.18 0.19
McKesson 0.25 0.27 0.26
CVS Health 0.27 0.29 0.28
Amazon.com 0.29 0.26 0.27
AT&T 0.27 0.30 0.28
Ford 0.22 0.31 0.25
GE 0.31 0.21 0.25
Mean 0.26 0.27 0.26

reference summaries. We evaluated the machine-generated
summary for each com-
pany against the reference summary (Letters to the
Shareholders) for that company.
Our goal in doing so was to assess the integrity of the machine-
generated summa-
ries. Tables 5 and 6 show ROUGE-1 scores for machine-
generated summaries
extracted to 10% and 20% of the word count of the original
Letter to the Sharehold-
ers, respectively.
Comparison of Human-Authored and Machine-Generated
Summaries. We used compara-
tive boxplots of ROUGE-1 F1-scores (see Tables 3-6) to
determine if there were any
observable differences between the human-authored summaries
and machine-generated
Table 4. Evaluation of Human-Authored Summaries Using
Walmart 0.10 0.49 0.17
Exxon Mobil 0.07 0.38 0.12
McKesson 0.11 0.48 0.17
CVS Health 0.11 0.48 0.18
Amazon.com 0.10 0.47 0.16
AT&T 0.11 0.48 0.17
Ford 0.14 0.49 0.21

Mean 0.10 0.47 0.17
Corporation Average recall Average precision Average F score
Walmart 0.05 0.50 0.10
Exxon Mobil 0.06 0.50 0.10
McKesson 0.05 0.50 0.10
CVS Health 0.06 0.50 0.11
Amazon.com 0.05 0.50 0.09
AT&T 0.05 0.50 0.09
Ford 0.05 0.50 0.10
Mean 0.05 0.50 0.10
Corporation Average recall Average precision Average F score
Walmart 0.10 0.50 0.17

Exxon Mobil 0.11 0.50 0.18
McKesson 0.10 0.46 0.16
CVS Health 0.11 0.50 0.19
Amazon.com 0.11 0.50 0.18
AT&T 0.10 0.50 0.17
Ford 0.10 0.50 0.16
Mean 0.10 0.50 0.17
summaries. Instead of analyzing precision and recall
individually, we focused our analy-
sis on the F1 scores because they represent the weighted
average of the two measures.
Our results are shown in Figures 3 and 4.
Field Expert Evaluation of Machine-Generated Summaries.
Finally, a reviewer of the arti-
cle wisely suggested that we seek input from financial experts
with regard to the effec-
tiveness of the machine-generated summaries. We solicited
open-ended feedback from
a convenience sample of eight financial experts, each of whom
held positions within
nationally or internationally recognized financial firms. Seven
of the eight invited par-
ticipants provided feedback on the reports.
We drew on discourse analysis principles to evaluate the
resulting feedback.
Specifically, we employed the discourse-based interpretive
content analysis method.
This method proposes a holistic approach not restricted by

coding rules, with the flex-
ibility to take context more fully into account (Ahuvia, 2001).
Although the responses
were not uniform, themes emerging from the analysis were
fairly homogeneous.
As a whole, the group commented that they would use the
summaries to make a
rapid determination as to whether to spend additional time and
energy reviewing the
Letter to Shareholders and the Annual Report. The reviewers
noted that the summaries
provided hints of insights into initiatives the companies are
pursuing, challenges faced
by the company, and overall perspectives with regard to the
organization’s culture and
values. As such, the summaries served a useful sorting function
as to which, if any,
reports the financial experts might examine in more depth. Each
reviewer was adamant,
however, that these documents provided financial information
that is at best dated.
From a structural perspective, the financial experts evaluated
the summaries for
overall coherence. They viewed 80% of the sample set as cogent
and coherent; the
other 20% was viewed as disjointed and difficult to interpret.
Discussion
The intention of this study was first to examine summarization

software from an over-
arching, semitechnical, almost pretheoretical perspective. After
that, we sought to evalu-
ate the effectiveness of summarization software and look for
important variances in the
data. These latter two areas enabled us to begin to answer two
key research questions:
Research Question 1: How effective is the summarization
software when sum-
marizing complex business reports?
Research Question 2: Are there any important variances
between outputs pro-
duced by human authors and artificial intelligence for the
selected data genre?
Summarization Software Effectiveness
Because ROUGE-1 is a recall-based measure based on content
overlap, it endeavors
to determine if the general concepts covered in an automatic
summary and a refer-
ence summary align (Allahyari et al., 2017). For summaries
comprising 10% of total
0
0.02
0.04
0.06
0.08
0.1

0.12
0.14
Human-authored Summaries Machine-generated Summaries
Figure 3. Boxplot of F1 scores for summaries capped at 10% of
word count of Letters to
the Shareholders.
0
0.05
0.1
0.15
0.2
0.25
Human-authored Summaries Machine-generated Summaries
Figure 4. Boxplot of F1 scores for summaries capped at 20% of
word count of Letters to
the Shareholders.
word count, ROUGE-1 metrics determined that approximately
21.9% of co-occur-
ring words within a given window in the human-authored

reference summaries were
also present in the machine-generated summary (see Table 1).
For summaries com-
prising 20% of total word count, ROUGE-1 metrics determined
that approximately
26.1% of unigrams in the human-authored reference summaries
were also present in
the machine-generated summary (see Table 2). ROUGE-1
metrics also determined
that machine-generated summaries have an approximately one-
fifth overlap with the
human-authored reference summaries comprising 10% of total
word count and one-
fourth overlap with the human-authored reference summaries
comprising 20% of
total word count. Combined with the overall F-measures, these
results suggest that
the automated text summarization tool is moderately sensitive
in terms of extracting
relevant instances of the text. Thus, while significant progress
has been made in the
field of natural language processing and computational
linguistics in the past six
decades, producing sophisticated advances in text
summarization (Das & Martins,
2007; Liu & Liu, 2009), much still needs to be accomplished in
the area of precision
and recall when summarizing complex business reports.
Variances in Human-Authored and Machine-Generated Outputs
The boxplots in Figures 3 and 4 above highlighted perceptible

differences between
the two summary categories. In Figure 3, the boxplots show that
the distribution for
the human-authored summaries were slight left-skewed, in
contrast to the distribu-
tion for the machine-generated summaries that were right-
skewed. In Figure 4,
however, the boxplots show a more symmetric distribution for
human-authored
summaries and a left-skewed distribution for the machine-
generated summaries.
Next, while the medians were relatively equal in both instances,
machine-generated
summaries exhibited tighter spreads than human-authored
summaries, indicative of
greater variability in the latter. The observations from the
boxplots, to some extent,
corroborate Steinberger and Jezek’s (2009) contention that a big
gap exists between
the summaries produced by automatic text summarizations
systems and summari-
zations generated by humans.
It is interesting to note that both summaries of Berkshire
Hathaway’s Letter to the
Shareholders (i.e., at 10% and 20% word count level) earned the
highest F-scores of
the 10 corporations evaluated when evaluated against their
corresponding human-
authored summaries (see Tables 1 and 2). The lowest F-scores,
on the other hand, were
earned by the summaries of ExxonMobil’s Letter to the
Shareholders. These results
prompted a qualitative inspection of the Letters to the
Shareholders of Berkshire
Hathaway and ExxonMobil to determine if there were any

distinguishing features,
apart from the fact that they operated in different industry
sectors.
Following are excerpts from the Letters to the Shareholders
delivered by the
Chairman and CEO of each of these corporations.
Warren Buffet, Berkshire Hathaway
Why the purchasing frenzy? In part, it’s because the CEO job
self-selects for “can-do”
types. If Wall Street analysts or board members urge that brand
of CEO to consider
possible acquisitions, it’s a bit like telling your ripening
teenager to be sure to have a
normal sex life. (2018, p. 4)
The bet illuminated another important investment lesson:
Though markets are generally
rational, they occasionally do crazy things. Seizing the
opportunities then offered does
not require great intelligence, a degree in economics or a
familiarity with Wall Street
jargon such as alpha and beta. What investors then need instead
is the ability to both
disregard mob fears, or enthusiasm, and to focus on a few
simple fundamentals. A
willingness to look unimaginative for a sustained period—or
even to look foolish—is
also essential. (2018, p. 12)
Darren Woods, ExxonMobil
ExxonMobil is in a prime position to generate strong returns
and remain the industry
leader, leveraging our strengths and outperforming our
competition in growing

shareholder value.
We’re investing in advantaged projects to grow our world-class
portfolio. Through
exploration and strategic acquisitions, we’ve captured our
highest-quality inventory since
the Exxon and Mobil merger, including high-impact projects in
Guyana and Brazil.
Integration enables us to capture efficiencies, apply
technologies, and create value that
our competitors can’t. (2018, p. 3)
A qualitative analysis of these excerpts reveals a distinctive
stylistic posturing in
the narrative of each letter. The Chairmen and CEO of
ExxonMobil employs a rigid,
formal writing style, which follows a conventional mechanical
formula that tradition-
ally characterizes official letters from the C-suite. His letter is
peppered with appro-
priate business conventions and familiar industry jargon such as
“leveraging our
strengths,” “strategic acquisitions,” “high-impact projects,” and
“integration enables
us to capture efficiencies.”
Warren Buffett, on the other hand, renowned for the folksy,
personal manner in
which he writes the company’s annual letter, employs a less
rigid, less formal style. On
the surface, Buffet’s style seems devoid of any artifice. He
infuses his letter with

unique words, creative phrases that are not traditionally used to
communicate informa-
tion formally in the business domain. Hence, his use of
analogies such as “It’s a bit like
telling your ripening teenager to be sure to have a normal sex
life” and statements such
as “They occasionally do crazy things” or “A willingness to
look unimaginative for a
sustained period—or even to look foolish—is also essential.”
The scores for the more traditionally postured Letter to the
Shareholders appear to
suggest that the summarization tool had greater success with
recall and precision when
the text strayed away from linguistic patterns that are common
and specific to the busi-
ness world. The likely conjecture from this is that extraction-
based automatic summa-
rization systems function less optimally when domain-specific
ontologies are employed.
Finally, evaluations of the machine-generated summaries by a
pool of financial
experts posit a favorable outlook for automated text
summarization tools. The respon-
dents overwhelmingly agreed that the machine-generated
summaries provided a sliver
of insight into the company’s operational performance and
strategic initiatives. These
insights were sufficient to trigger a go/no go decision in terms
of further exploration
of the original document.
Conclusion and Future Studies
The results of this study show that the extraction-based

summarization system pro-
duced moderately satisfactory results in terms of extracting
relevant instances of the
text from the business reports. Much work still needs to be
accomplished in the area of
precision and recall in extraction-based systems before the
software can match a
human’s ability to capture the gist of a body of text.
But beyond practical applications, automatic text summarization
highlights a
broader discourse. Automatic text summarization raises
important issues connected to
AI and cognitive science. Therefore, further study into how
advanced text summariza-
tion capability affects cognitive capacity and intelligence may
augment our ability as
communication professionals to both disseminate and consume
information more effi-
ciently. Additional text corpora covering different data genres
should be empirically
evaluated to obtain more robust findings.
From a business communication perspective, we best agree that
this form of com-
munication technology is not going away. The effectiveness of
the text summarization
software may only be between 22% and 26%, but it is not going
to get lower. Instead,
the field should remain alert to future developments of this
software and look for ways

by which to incorporate it into future studies as well as class
teachings.
Finally, and perhaps most important, our findings hint at a
forthcoming synergy
between what AI does and what business leaders proclaim to
desire. At its heart, AI
depends on consistency, pattern recognition, and logical
development, even when deal-
ing with summarization software. Christensen (2015)) and many
other business experts,
on the other hand, argue vociferously in favor of creativity and
new ideas for business
models. When presented with the creativity of a Warren
Buffett—or, more directly,
when presented with a letter written differently from other
patterns—the AI summari-
zation software proved to be very effective. In fact, when
compared against a human
gold standard, AI proved demonstrably better at extracting
Berkshire-Hathaway’s cre-
ative syntax than it did ExxonMobil’s business jargon-laded
language. This synergy
bodes well for AI’s role in business communication and
business in general.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship,
and/or publication of this article.
Funding
The author(s) received no financial support for the research,
authorship, and/or publication of

this article.
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Author Biographies
Jefrey Naidoo is an assistant Professor of Management and the
Derrell Thomas Faculty Fellow
at the University of Alabama. His research focuses on how
visual analytics and artificial intel-
ligence may be leveraged to help organizations navigate big
data and meet business intelligence
objectives.
Ronald E. Dulek is the John R. Miller Professor of Management

at the University of Alabama.
He is a longtime supporter of the Association for Business
Communication and a past recipient
ofthe Kitty O. Locker Award.
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Artificial intelligence in healthcare: a review on predicting
clinical needs
Djihane Houfani, Sihem Slatnia, Okba Kazar , Hamza Saouli
and Abdelhak Merizig
LINFI Laboratory, University of Biskra, Biskra, Algeria
ABSTRACT
Artificial Intelligence is revolutionizing the world. In the last
decades, it is applied in almost all
fields especially in medical prediction. Researchers in artificial
intelligence have exploited
predictive approaches in the medical sector for its vital
importance in the process of
decision making. Medical prediction aims to estimate the
probability of developing a
disease, to predict survivability and the spread of a disease in
an area. Prediction is at the

core of modern evidence-based medicine, and healthcare is one
of the largest and most
rapidly growing segments of AI. Application of technologies
such as genomics,
biotechnology, wearable sensors, and AI allows to:
(1) increase availability of healthcare data and rapid progress of
analytics techniques and
make the foundation of precision medicine;
(2) progress in detecting pathologies and avoid subjecting
patients to intrusive examinations;
(3) make an adapted diagnosis and therapeutic strategy to the
patient’s need, his
environment and his way of life.
In this research, an overview of applied methods on the
management of diseases is presented.
The most used methods are Artificial Intelligence methods such
as machine learning and deep
learning techniques which have improved diagnosis and
prognosis efficiency.
ARTICLE HISTORY
Received 6 March 2020
Accepted 26 December 2020
KEYWORDS
Predictive medicine; artificial
intelligence; prediction;
healthcare; diagnosis;
prognosis; breast cancer;
cardiovascular diseases
1. Introduction

The healthcare domain is facing many challenges. In
particular, handling large amounts of data (Big Data)
will be a critical issue due to its sensibility. Also,
these data are growing continuously and are some-
times more complex which need diagnosis time and
rising costs. In fact, every area has been impacting
most healthcare providers and patients [1]. Predictive
medicine is a field of medicine, which consists of
determining the probability of disease. Its main role
is to decrease the impact upon the patient such as by
preventing mortality or limiting morbidity. Despite
the several proposed solutions, medical prediction
remains a challenging task and demands a lot of
efforts. This is attributed to its vital importance in
decision making. The main goals of predictive medi-
cine are: (i) the practice of collecting and cataloguing
characteristics of patients (big data analytics) [2]; (ii)
analyzing that data to predict the patient’s individual
risk for an outcome of interest; (iii) predicting which
treatment in which individual will be most effective,
and then intervening before the outcome occurs.
Actually, Medical Informatics is at the junction of
the disciplines of medicine and information technol-
ogy and artificial intelligence tools. Both of these con-
cepts play a crucial role in advancing the science of
quality measurement. Artificial intelligence
technologies provide multiple services. They are used
to improve accuracy, efficiency and public health,
and maintain privacy and security of patient health
information. The rest of the paper is organized as fol-
lows. Section 2 introduces the predictive medicine
domain. Section 3 describes some proposed works in
medical prediction domain. Section 4 elaborates a
comparative study of described works. Then, we

finish with a discussion and a conclusion in Section 5.
2. Predictive medicine
Medicine is undergoing a revolution that virtualizes
most medical practices. This revolution is emerging
from the convergence of biology and medicine and
computer technology with its ability to analyze ‘big
data’ sets, deploy this information in business and
social networks and create digital consumer devices
measuring personal information [3].
Predictive medicine is a field of medicine that esti-
mates the likelihood of disease occurring in the future
taking into account relevant risk factors such as age,
sex, clinical measured data, and so on. New technol-
ogies allow to characterize infectious agents more
rapidly and to produce effective vaccines more
quickly. To generate predictive models of health and
disease for each patient, researchers are developing
© 2021 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Djihane Houfani [email protected] LINFI
Laboratory, Department of Computer Science, University of
Biskra, P.O. Box 145
RP, 07000 Biskra, Algeria
INTERNATIONAL JOURNAL OF HEALTHCARE
MANAGEMENT
2022, VOL. 15, NO. 3, 267–275
https://doi.org/10.1080/20479700.2021.1886478
http://crossmark.crossref.org/dialog/?doi=10.1080/20479700.20
21.1886478&domain=pdf&date_stamp=2022-07-22
http://orcid.org/0000-0003-0522-4954

mailto:[email protected]
http://www.tandfonline.com
powerful new tools by exploiting artificial intelligence
technology and biology techniques.
3. Literature review
Janghel et al. [4] developed a system for diagnosis,
prognosis, and prediction of breast cancer (BC)
using ANN models to assist doctors. Four models of
neural networks were used to implement this system:
Back Propagation Algorithm (MLP), Radial Basis
Function Networks (RBF), Learning vector Quantiza-
tion (LVQ), and Competitive Learning Network (CL).
LVQ gave the best accuracy in the testing data set.
However, the performed experiments of this work
were limited to single database with a limited attri-
butes for breast cancer.
Vikas and Saurabh [5] proposed diagnosis system
for detecting BC based on three data mining tech-
niques RepTree, RBF Network and Simple Logistic.
These algorithms were used to predict the survivability
rate of breast cancer data set. The three classification
techniques were compared to find the most accurate
one for predicting cancer survivability rate. The data
used in this study were provided by the University
Medical Centre, Institute of Oncology, Ljubljana,
Yugoslavia. Authors used WEKA software to
implement the machine learning algorithms.
The objective of Bichen et al. [6] in this research
was to diagnose breast cancer by extracting tumor fea-
tures. Authors developed a hybrid of K-means and

SVM algorithms to extract useful information and
diagnose the tumor. The K-means algorithm was uti-
lized to recognize the hidden patterns of the benign
and malignant tumors separately. Then, to obtain a
new classifier an SVM was used.
Karabatak and Cevdet Ince [7] proposed an auto-
matic diagnosis system based on associative rules
(AR) and neuronal network for detecting breast can-
cer. This method consisted of two stages. In the first
stage, association rules were used to reduce the input
feature vector dimension. Then, in the second stage
neural network used these inputs and classified the
breast cancer data. This method worked well; how-
ever, it performs poorly if the features are not chosen
well.
Seera and Lim [8] proposed a hybrid intelligent sys-
tem based on Fuzzy Min–Max neural network, the
Classification and Regression Tree, and the Random
Forest (RF) model for undertaking medical data
classification problems. This system had two impor-
tant practical implications in the domain of medical
decision Support: accuracy and the ability to provide
explanation and justification for the prediction. The
results were evaluated using three benchmark medical
data sets.
Nilashi et al. [9] developed a knowledge-based sys-
tem for the classification of breast cancer disease using
Expectation Maximization (EM), Classification and
Regression Trees (CART), and Principal Component
Analysis (PCA). The proposed system can be used as
a clinical decision support system to assist medical
practitioners in the healthcare practice.

Nguyen et al. [10] proposed a computer-aided diag-
nostic system to distinguish benign breast tumor from
malignant one. Their method consisted of two stages
in which a backward elimination approach of feature
selection and a learning algorithm RF are hybridized.
The average obtained classification accuracy was
between 99.70 and 99.82% in test phase applied for
Wisconsin Breast Cancer Diagnosis Dataset (WBC-
DD) and Wisconsin Breast Cancer Prognostic Dataset
(WBCPD). This result indicated that the proposed
method can be applied to other breast cancer pro-
blems with different data sets especially with ones
that have a higher number of training data. However,
RF becomes slow and ineffective for real-time predic-
tions when a large number of trees are generated.
Ahmed et al. [11] developed a Computer-Aided
Diagnosis (CAD) scheme for the detection of breast
cancer using deep belief network (DBN) unsupervised
path followed by back propagation supervised path.
The proposed system was tested on the Wisconsin
Breast Cancer Dataset (WBCD) and gave an accuracy
of 99.68%. However, this approach was computation-
ally expensive.
Thein and Tun [12] proposed a breast cancer
classification approach. This approach was based on
the Wisconsin Diagnostic and Prognostic Breast Can-
cer and the classification of different types of breast
cancer datasets. The proposed system implemented
the island-based training method to obtain better
accuracy and less training time by using and analyzing
between two different migration topologies. However,
in this method same parameters may not guarantee
the global optimum solution.

Arpit et al. [13] proposed a GONN algorithm, for
solving classification problems. This algorithm was
used to classify breast cancer tumors as benign or
malignant. To demonstrate their results, authors
took theWBCD database fromUCIMachine Learning
repository and compared the classification accuracy,
sensitivity, specificity, confusion matrix, ROC curves,
and AUC under ROC curves of GONN with classical
model and classical Back propagation model. How-
ever, in this algorithm, only crossover and mutation
operators were improved and it was applied only on
WBCD database.
Dheeba et al. [14] proposed a new classification
approach for the detection of breast abnormalities in
digital mammograms using Particle Swarm Optimized
Wavelet Neural Network (PSOWNN). The proposed
work was based on extracting Laws Texture Energy
Measures from the mammograms and classifying the
suspicious regions by applying a pattern classifier
268 D. HOUFANI ET AL.
and applied to real clinical database. However,
PSOWNN method suffers from difficulty in finding
their optimal design parameters.
Raúl Ramos-Polĺan et al. [15] proposed and evalu-
ated a method to design mammography-based
machine learning classifiers (MLC) for breast cancer
diagnosis. This method allowed to characterize breast
lesions according to BI-RADS classes (grouped by
benign and malignant). This approach gave a good

accuracy but it was evaluated on one database.
Geert Litjens et al. [16] explored deep learning to
improve the objectivity and efficiency of histopatholo-
gic slide analysis. Authors used convolutional neural
network to digitized histopathology through two
different experiments: prostate cancer detection in
hematoxylin and eosin (H&E)-stained biopsy speci-
mens and identification of metastases in sentinel
lymph nodes obtained from breast cancer patients.
This method gave accurate results but it showed
some detection errors in the prostate cancer exper-
iment and data were extracted from a single center.
This approach was performing in terms of accuracy
but it was computationally expensive.
Wang et al. [17] proposed a deep learning based
approach for detecting metastatic breast cancer from
whole slide images of sentinel lymph nodes. This
approach was tested on Camelyon16 dataset. The pro-
posed approach improved in the reproducibility, accu-
racy, and clinical value of pathological diagnoses;
however, it was computationally expensive.
Gonźalez-Briones et al. [18] designed a multi-
agents based system to manage information of
expression arrays. In this system, different data mining
techniques and databases were used to analyze
expression profiles; its aim was to provide genes that
show differences between samples from younger and
older patients to discover why older women respond
better to the treatment. The system identified the
genes that can be therapeutic targets. However, for a
best result, it is necessary to check if the gene in ques-
tion is over or under-expressed.

Cruz-Roa et al. [19] proposed a deep learning based
tool that employed a convolutional neural network
(CNN) to detect automatically presence of invasive
tumors on digitized images. This approach was tested
on data from different sources. However, while using
this method, some breast cancer regions were incor-
rectly classified.
In this paper, Ankur and Jaymin [20] proposed a
predictive model for heart disease detection using
Machine Learning and Data Mining techniques. The
proposed approach combined between Naive Bayes
(NB) and Genetic Algorithm (GA) to classify heart
diseases. Data were collected from Cleveland Heart
Disease Data set (CHDD) available on the UCI Repo-
sitory. Nonetheless, this model could not predict
specific heart disease.
In this paper, Vignon-Clementel et al. [21] pro-
posed a 3D simulation approach for blood flow and
arterial pressure, this method has been applied to cal-
culate hemodynamic quantities in various physiologi-
cally relevant cardiovascular models, including
patient-specific examples, to study non-periodic flow
phenomena, often seen in normal subjects and in
patients with acquired or congenital cardiovascular
disease. However, it was difficult to measure pressures
and flow rates in vivo simultaneously and it was feas-
ible in a very limited number of research cases. Fur-
thermore, the vessel wall displacements were
overestimated because of resistance boundary
condition.
In this paper, Subanya et al. [22] used meta-heuris-
tic algorithm (bee colony) to determine the subset of
optimal characteristics with better classification accu-

racy in the diagnosis of cardiovascular disease. Data
were taken from UCI repository (a database of cardi-
ovascular diseases).
Shaikh et al. [23] used ANNs to predict the medical
prescription of heart disease. This work included
detailed information about the patient’s symptoms
and the pretreatment that was done. Doctors can
also use this web-based tool for the diagnosis of
heart disease using the basic radial function. Outputs
of this system have been compared with the prescrip-
tions of the doctors and it was satisfactory.
In this paper, Singh et al. [24] applied Structural
Equation Model (SEM) to identify the strength of
relationships among variables that are considered
related to the cause of Cardiovascular Diseases
(CVDs) and Fuzzy Cognitive Map (FCM) to evaluate
obtained results in a predictive system that helps for
the detection of people who are at risk of developing
CVDs. In this study, data have been extracted from
Canadian Community Health Survey (CCHS) data
source. However, authors did not use enough attri-
butes to have a very accurate model.
Singh et al. [24] proposed a predictive system of
CVDs using quantum neural network (QNN) for
machine learning. Data were extracted from 689
patients showing symptoms of CVD and the dataset
of 5209 CVD patients of the Framingham study.
This system had been experimentally evaluated and
compared with Framingham risk score (FRS). This
proposed system predicted the CVD risk with high
accuracy and was able to update itself with time.
In this paper, Venkatalakshmi et al. [25] designed

and developed diagnosis and prediction system for
heart diseases. In this system, prediction was based
on two algorithms: DT and NB were executed on
Weka tool; dataset consisted of attributes and values
which are collected from UCI machine learning repo-
sitory which is a repository of databases, domain the-
ories, and data generators. In order to improve the
efficiency and accuracy, an optimization process
MANAGEMENT 269
genetic algorithm has been used. In this system, a large
amount of data were used that must be reduced and
take into consideration only subset of attribute
sufficient for heart disease prediction.
Boden et al. [26] proposed a mathematical method
to predict the probability of surgery prior to the first
visit based on a sample of 8006 patients with low
back pain. Independent risk factors for undergoing
spinal surgery were identified by using univariate
and multivariate statistical analysis, and the Spine Sur-
gery Likelihood (SSL) model was created using a ran-
dom sample of 80% of the total patients in the used
cohort, and validated on the remaining 20%. However,
this method was unable to track patients who have
undergone surgery in a different facility and, therefore,
may have been misclassified in the non-surgical group.
In this paper, Søreide et al. [27] proposed an
approach that used Artificial Neural Network
(ANN), multilayer perceptron (MLP) to predict the
mortality of patients with perforated peptic ulcer.

Input to this approach was a sample of patients ana-
lyzed by Statistical Package for Social Sciences (IBM
SPSS v. 21, Inc. for Mac). Its principle was to propose
three models of MLP and give the model with the opti-
mal performance. However, in this kind of
approaches, the intervention of the human expert is
essential for the collection of data and garbage-in, gar-
bage out problem can exist.
Nyssa et al. [28] proposed, in their article, a predic-
tive model of rabies in Tennessee; it was based on
spatial analysis. The proposed method consisted of:
(1) Data acquisition from the Tennessee’s Health
Department
(2) Data processing using ArcGIS software to get the
predictive model
(3) Spatial analysis using Fragstats and Circuitscape
software.
Result of this system was a set of models (maps)
such as distribution models, density model and so
on. However, it did not allow a real-time disease’s sur-
veillance and was not efficient in case of companies
with large population.
In this paper, Sharmila Devi et al. [29] described in
this paper a distributed system of e-health for the
automatic diagnosis of the situation of a patient
based on his data without the participation of a doctor.
This service was provided on the Internet. When a
patient’s situation changes, the system will automati-
cally alert the doctor. This has been implemented

using Multi-Agent System (MAS) and Adaptive
Neuro-Fuzzy Inference System (ANFIS). The different
agents in the system were in different places and used
an asynchronous communication to communicate
each other.
In this paper, Kaberi et al. [30] presented an
approach that consisted of hybridization between
GA, harmony search algorithms (HAS) and support
vector machine (SVM) for the selection of informative
genes. However, heuristic methods depend on the pro-
blem and they are generally based on a local optimum
that fails to obtain the optimal overall solution.
Golnaz et al. [31], in this paper, proposed a feature
selection method based on a genetic algorithm. To
evaluate the subsets of the selected characteristics,
the k nearest neighbors (KNN) classifier was used
and validated on a set of data of the UCI database.
In this paper, Talayeh et al. [32] used unbalanced
classification techniques: NB, Radial Basis Function
Neural Network (RBFNN), 5-Nearest Neighbors,
Decision Trees (DT), SVMs, and Logistic Regression
(LR) to identify the complications of bariatric surgery
for each patient. The combination of classification
methods made possible to achieve higher performance
measures (Figure 1).
3.1. Breast cancer prediction and diagnosis
In this section, we discuss researches which used
different AI methods to manage breast cancer disease.
Table 1 summarizes the reviewed work dealing with
Figure 1. Flow diagram that summarizes the reviewed

researches.
Table 1. Summary table of researches which used different
techniques to manage breast cancer disease.
Works Objective Method Data Result Limitations
Janghel et al. [4] Diagnosis (malignant and
benign cells classification)
ANN (application of 4 methods) WBCD (collected data) Best
classification method
(LVQ)
Use of one dataset with limited attributes
Chaurasia et al.
[5]
Diagnosis/prognosis
(survivability prediction)
Data mining (Rep tree, RBF
network, simple logistic)
University Medical Centre, Institute of oncology
Ljubljana Yugoslavia
Best method (simple logistic) Use of one dataset with limited
attributes
Zheng et al. [6] Diagnosis K-means and SVM classifier WBCD
(table: attributes-values) Features selection for tumors

classification
It is not implemented in a large-scale sparse data set
Karabatak et al.
[7]
Diagnosis AR and neural network WBCD (table: attributes-
values) Tumors classification Applied on one dataset
Seera et al. [8] Medical data classification FMin-MaxNN,
Classification and
Regression Tree, RF model
WBCD, Pima Indians Diabetes, and Liver Disorders
from the UCI Repository of Machine Learning
Undertaking medical data
classification problems
Good
Nilashi et al. [9] Diagnosis - EM for data clustering
- Fuzzy logic for data
classification
- PCA to solve multi-collinearity
problem
- CART for automatic fuzzy rules
generation
- WBCD (table: attributes-values)
- Mammographic mass dataset
Tumors classification EM fails on high-dimensional data sets
due to
numerical precision problems

Nguyen et al. [10] Diagnosis and prognosis Feature selection
RF classifier
WBCDD and WBCPD Tumor classification RF becomes slow
and ineffective for real-time
predictions when a large number of trees are
generated
Abdel-Zaher et al.
[11]
Diagnosis DBN (unsupervised) for pre-
training
Supervised back propagation for
classification
WBCDD Tumor classification Computationally expensive
Thein et al. [12] Diagnosis Differential evolution algorithm
(for training)
Parallelism
WBCDD A neural network for Tumor
Classification
Same parameters may not guarantee the global
optimum solution
Bhardwaj et al.
[13]
Diagnosis GONN WBCDD Tumor classification - Only
crossover and mutation operators are improved
- Applied on one dataset

Dheeba [14] Diagnosis PSOWNN Mammogram screening center
(real data `a images) BC detection - Dependency on initial point
and parameters.
- Difficulty in finding their optimal design parameters
Ramos-Polĺan
et al. 15]
Diagnosis Machine learning classifier BCDR ML classifiers
Evaluated on one database
Litjens et al. [16] Diagnosis Deep learning (CNN) Collected
patient’s specimens Histopathologic slide analysis
Computationally expensive
Wang et al. [17] Diagnosis Deep learning Camelyon16 dataset
Cancer metastases
identification
Gonźalez-Briones
et al. [18]
Prognosis MAS
Deep learning
Samples provided by Salamanca Cancer Institute Gene selection
Cruz-Roa et al.
[19]
Diagnosis CNN Digital images from different institutions
Invasive breast cancer
classification

Some errors of classification
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271
breast cancer disease. The first column refers to the
investigated work; the second column is the objective
of the work; the third column is the used method to
handle the disease; the fourth column refers to the
used dataset of the paper; the fifth column consists
of the results; and finally, the last one refers to the
limitations of the proposed work.
3.1.1. Discussion
Breast cancer is the most common cause of women’s
deaths worldwide [33]. It is a result of mutations,
anarchic division, and abnormal changes of cells.
AI applies algorithms on a large volume of health-
care data to assist clinical practice. These algorithms
show their ability to improve accuracy by learning
and self-correcting.
After observing the reviewed researches that man-
age breast cancer disease, we can notice that machine
learning techniques are widely used in diagnosis,
tumors classification and breast cancer prediction to
assist physicians in decision making process and
early detection. The most used dataset is WBCD
from UCI Repository. These works show a good per-
formance in terms of accuracy. However, some techni-
cal problems can be considered:
(1) Computational and memory expenses
(2) Data availability: Training AI systems requires

large amounts of structured and comprehensive
data. However, the available data are fragmented,
incomplete and unstructured, these problems
increase the risk of error
(3) Overfitting problem: This occurs when the model
properly fits the training data and encounters
difficulties for generalization on new or unseen
data (validation data).
(4) Reproducibility issue: A study is reproducible
when others can replicate the results using the
same algorithms, data and methodology.
3.2. Other diseases
Researches mainly concentrate around diseases which
are leading causes of death. We can classify them into
the following types: cardiovascular disease, cancers,
viral disease, and nervous system disease; therefore,
early diagnosis and prognosis are fundamental to pre-
vent the deterioration of patients’ health status.
Table 2 summarizes the reviewed work dealing with
different diseases. The first column refers to the inves-
tigated work; the second column is the tackled disease;
the third column is the used method to handle the dis-
ease; the fourth column refers to the objective of the
paper; the fifth column consists of the used dataset;
and finally, the last one refers to the achieved perform-
ance of the proposed work.
3.2.1. Discussion
The use of artificial intelligence techniques in medical

prediction to manage different diseases shows a
Table 2. Summary table of researches which used different
techniques to manage multiple diseases.
Works Disease Method Objectives Input Performance
Makwana et al. [20] CVD ML and Data Mining Heart disease
detection Cleveland Heart
Disease Data set
Good but it can be
improved
Vignon et al. [21] Cardiovascular
system
- Mathematic
equation
- Analog electrical
circuit
3D simulation approach for
blood flow and arterial
pressure
Measured data Its validation is proven
in vitro and in vivo
data
Subanya et al. [22] CVD Meta-heuristic
algorithm (bee
colony)
CVD Classification UCI repository Good
Hannan et al. 23] CVD ANN Medical prescription of heart

disease prediction
Patient information Good
Singh et al. [24] Cardiovascular
disease
SEM and FCM Building a Cardiovascular
Disease Predictive Model
CCHS dataset It can be improved
Narain et al. [25] CVD QNN Risk of CVDs prediction Patients
with CVDs Good but it can be
improved
Venkatalakshmi
et al. [26]
CVD DT and NB Heart diseases prediction Attributes and
values
from UCI database
Good but it can be
improved
Boden et al. [27] Orthopedic surgery Mathematical method
Surgery prior’s probability
prediction
Patient-reported data Low level of evidence (4)
Søreide et al. [28] Gastric disease ANN modeling Mortality
prediction for
patients with

Gastric disease ANN modeling
Nyassa et al. [29] Viral disease spatial analysis Rabies
prediction in
Tennessee
Tennessee’s Health
Department
Good accuracy
Devi et al. [30] Neck and arm pain
disease
MAS and ANFIS Patients automatic diagnosis Patient-reported
data Good
Das et al. [31] Informative genes
selection
GA, HAS and SVM Selection of informative genes Gene
expression
dataset
Good
Sahebi et al. [32] Feature selection
method
GA Feature selection and
classification optimization
UCI Arrhythmia
database,
Good

Razzaghi et al. [33] Bariatric surgery Imbalanced
classification
techniques
Identify bariatric surgery’s
complications
The Premier
Healthcare
Database
Good
performance improvement in terms of accuracy, speed
and interoperability. Machine Learning techniques are
suitable for the management of multiple diseases
(Figure 2). Furthermore, their use makes disease man-
agement more reliable by reducing diagnosis and
therapeutic errors, and extracting useful information
from large amount of data to predict health outcomes.
Multiple data are used in these researches such as
medical images, patient’s reported, data datasets
from UCI Repository and several public datasets.
3.3. Application of AI in healthcare: General
challenges
This paper shows that Artificial intelligence brings
important developments to health-care field, however,
a subsequent research challenges remaining:

(1) Data quality and availability: Acquiring large
amounts of high-quality clinical datasets is a
very difficult process, because they are in multiple
formats and fragmented across different systems
and generally have limited access [34].
(2) Security and privacy issue: Several researchers
have been interested in this concept and have pro-
posed work to manage data security [35] because
it is one of the biggest challenges facing AI sys-
tem’s developers. The requirement of large
amounts of data from many patients may affect
their data privacy.
(3) Bias issue: AI systems learn to make decisions
based on training data which can include biases.
(4) Computational cost: Most reviewed works are
computationally expensive, which is not beneficial
for both clinician and patient.
(5) Interpretability: The most important task in the
healthcare domain is evaluating and validating
the proposed approach to be accepted by the
community.
(6) Injuries and error: An AI system may be some-
times wrong by failing in diseases prediction or
in a drug recommendation or in predicting the
response of a patient to a specific treatment.
These failures can occur patient injury or other
healthcare problems.
4. Conclusion

Medical prediction is a very important challenge for
clinicians because it has a direct influence on their
daily practice. In the last decade, the death rate
increases significantly, this required methods and
tools for accurate and early detection of diseases.
While going through literature review, we noticed
that researchers are interested in medical prediction
especially in the diagnosis and prognosis of breast can-
cer using methods and approaches of artificial intelli-
gence such as ANN, deep learning and data mining,
and so on. The authors in the literature proposed sys-
tems and compared them to other existing works. We
can note that their approaches are efficient in terms of
accuracy; however, most of them are time-consuming
in the training phase. We can also notice that very few
of these research works have actually been integrated
the clinical practice.
In this paper, we discussed the biggest challenges
facing the application of AI in the healthcare field.
To handle these challenges, several solutions can be
proposed:
(1) High-quality data generation and availability: To
build an efficient AI system it is important to pro-
ceed on good datasets, that’s why it is important
to create high-quality databases accessible by
researchers and AI systems developers in a man-
ner consistent with protecting patient privacy.
Blockchain technology can be used to secure per-
sonal and medical data [36].
(2) Quality supervising: Good training and validating
of AI systems will help address the risk of errors
and patient injury.

(3) Good exploitation of AI methods: Hybridization
of deep learning method with optimization algor-
ithms [37], parallelization, could be powerful for
time and cost reduction. Big data analytics also
offers several opportunities in this field [38].
The used techniques in reviewed works include
mathematical methods, evolutionary computing,
case-based reasoning, fuzzy logic, ANNs, data mining,
machine learning, deep learning, and intelligent
agents. However, the medical prediction is not wide-
spread due to several constraints. Hence comprehen-
sive research needs to be done in this sphere keeping
an eye towards developing hybrid techniques that
could be employed to predictive medicine. The selec-
tion of the appropriate technique is important for
developing and implementing disease diagnosis sys-
tems. As a perspective of this work, we aim to designFigure 2.
Used techniques in medical literature.
MANAGEMENT 273
our medical predictive approach based on deep
reinforcement learning and genetic algorithms to
improve breast cancer diagnostic performance. Fur-
thermore, to overcome big data problems, the number
of characteristics in the dataset must be reduced which
allows ensuring the quality of data (QoD). The advan-
tage of developing deep learning technique for the
management of breast cancer disease will be reached
by applying it as support tools that help physicians
in diagnosis, prognosis, and treatment. By using this
type of systems reading variability by physicians will

be eliminated. Besides, more quick and accurate diag-
nosis will result.
Despite the several challenges facing AI application
in healthcare field, it is very promising in decision-
making aid, physician and patient medical support,
and prediction and we believe there are still significant
perspectives on this topic.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes on contributors
Djihane Houfani received the Licence andMaster degrees in
Computer Science from University of Biskra, Algeria in
2015 and 2017, respectively. She is now a PhD student in
artificial intelligence at the University of Biskra and her cur-
rent research interest includes medical prediction, deep
learning, multi-agent systems and optimization.
Sihem Slatnia was born in the city of Biskra, Algeria. She
followed her high studies at the university of Biskra, Algeria
at the Computer Science Department and obtained the
engineering diploma in 2004 on the work “Diagnostic
based model by Black and White analyzing in Background
Petri Nets”, After that, she obtained Master diploma in
2007 (option: Artificial intelligence and advanced system’s
information), on the work “Evolutionary Cellular Automata
Based-Approach for Edge Detection”. She obtained PhD
degree from the same university in 2011, on the work “Evol-
utionary Algorithms for Image Segmentation based on Cel-
lular Automata”. Presently she is an associate professor at
computer science department of Biskra University. She is
interested to the artificial intelligence, emergent complex

systems and optimization.
Okba Kazar professor in the Computer Science Department
of Biskra, he helped to create the laboratory LINFI at the
University of Biskra. He is a member of international con-
ference program committees and the “editorial board” for
various magazines. His research interests are artificial intel-
ligence, multi-agent systems, web applications and infor-
mation systems.
Hamza Saouli received the Master and Doctorate degrees in
Computer Science from University of Mohamed Khider
Biskra (UMKB), the Republic of Algeria in 2010 and 2015,
respectively. He is a university lecturer since 2015 and his
research interest includes artificial intelligence, web services
and Cloud Computing.
Abdelhak Merizig obtained his Master degree by 2013 from
Mohamed Khider University, Biskra, Algeria, He is working
on an artificial intelligence field. He obtained his PhD
degree from the same university in 2018. Abdelhak Merizig
is now a university lecturer at the computer science depart-
ment of Biskra University. Also, he is a member of LINFI
Laboratory at the same University. His research interest
includes multi-agent systems, service composition, Cloud
Computing and Internet of Things.
ORCID
Okba Kazar http://orcid.org/0000-0003-0522-4954
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