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Artificial intelligence in AI in
healthcare
healthcare: a comprehensive
review of its ethical concerns
Chokri Kooli 121
University of Quebec at Outaouais, Gatineau, Canada and
The University of Ottawa, Ottawa, Canada, and Received 23 December 2021
Revised 8 February 2022
Hend Al Muftah Accepted 8 February 2022

Doha Institute for Graduate Studies, Doha, Qatar

Abstract
Purpose – Nowadays, the digitized economy and technological advancements are increasing at a faster pace.
One such technology that is gaining popularity in the healthcare sector is Artificial Intelligence (AI). AI has
been debated much, searched so well due to the implications, issues and for its benefits in terms of ease, it will
offer. The following research has focused on examining the ethical dilemmas associated with AI when it will be
introduced in the healthcare sector.
Design/methodology/approach – A narrative review method focusing on content analysis has been used in
the research. The authors have employed a deductive approach to determine the ethical facets of adopting AI in
the healthcare sector. The current study is complemented by a review of related studies. The secondary data
have been collected from authentic resources available on the Internet.
Findings – Patient privacy, biased results, patient safety and Human errors are some major ethical dilemmas
that are likely to be faced once AI will be introduced in healthcare. The impact of ethical dilemmas can be
minimized by continuous monitoring but cannot be eliminated in full if AI is introduced in healthcare. AI
overall will increase the performance of the healthcare sector. However, we need to address some
recommendations to mitigate the ethical potential issues that we could observe using AI. Technological change
and AI can mimic the overall intellectual process of humans, which increases its credibility and also offers harm
to humans.
Originality/value – Patient safety is the most crucial ethical concern because AI is a new technology and
technology can lead to failure. Thus, we need to be certain that these new technological developments are
ethically applied. The authors need to evaluate and assess the organizational and legal progress associated
with the emergence of AI in the healthcare sector. It also highlights the importance of covering and protecting
medical practitioners regarding the different secondary effects of this artificial medical progress. The research
stresses the need of establishing partnerships between computer scientists and clinicians to effectively
implement AI. Lastly, the research highly recommends training of IT specialists, healthcare and medical staff
about healthcare ethics.
Keywords Artificial intelligence, Public health, Ethics, Social policy, Sustainability
Paper type Research paper

Introduction
The term Artificial Intelligence (AI) refers to the processes through which a system can mimic
human intellectual processes, such as reasoning ability, decision-making, generalization, or
learning from prior experiences, to accomplish objectives without being explicitly programmed
for particular actions (Copeland, 2020). In contrast to the intelligence of humans or other living
species, AI is defined as the intelligence of machines (Rong et al., 2020). Therefore, AI may
include the fields of machine learning, natural language processing and robotics, that can be
applied to almost any field in medicine, and it potentially contributes to biomedical research,
medical education and delivery of healthcare (Ramesh et al., 2004). AI also helps in those
situations in which machines can simulate like a human mind in learning processes and analysis Technological Sustainability
and thus can help in proper diagnosis, solving the problems and effective decision-making. Vol. 1 No. 2, 2022
pp. 121-131
Rong et al. (2020) admitted that the application of AI in healthcare sectors considerably © Emerald Publishing Limited
2754-1312
accelerated in the recent years. Thus, Eugenio Zuccarelli (2020) admitted that in 2019, the US DOI 10.1108/TECHS-12-2021-0029
TECHS investment in AI through healthcare was equal to $ 2,487.7 billion. Recent research study
1,2 published by Reports and Data (2021) estimates that the global Compound Annual Growth
Rate (CAGR) of the AI investments in healthcare market will continue to grow considerably.
Their estimations forecast a market size of $ 61.59 billion by 2027. From his side, Zakaryan
(2021) estimates that the CAGR of the AI investments in healthcare market will increase by
41.8% and will reach the level of $ 120.2 billion in 2028.
The increased AI deployment and investment in data protection in healthcare sector will
122 revolutionize the system and could be explained in several ways. Researchers (AlAhmad
et al., 2021; Sidhom, 2021; Barman et al., 2021) believe that the application of AI will improve
the, efficiency and performance of the healthcare system. As example, the relay on AI could
enhance oncological diagnosis. Also, the relay on technology will participate in improving the
quality of the offered services and reduce their costs. The latest Covid-19 pandemic showed
the increased recourse to telemedicine which also showed a broadening access to healthcare
delivery (B
elisle-Pipon et al., 2021; Spatharou et al., 2020; Lee and Yoon, 2021). A recent report
of the Organisation for Economic Co-operation and Development (OECD) (2020) admitted
that the healthcare sector is a wasteful one and is being disqualified as inefficient. This
increased recourse to AI in health system administration and service redesign (OECD, 2020).
Other researchers (AlAhmad et al., 2021; OECD, 2020; Barman et al., 2021; Dilsizian and
Siegel, 2014) showed that the adoption of AI will greatly assist in clinical decision-making.
With the advancement in technology, AI has been one of the most debated aspects to be
used in the healthcare sector (Kooli, 2021). As explained by Zandi et al. (2019), AI in healthcare
is an overarching term that is being used to describe the utilization of software, machine
learning algorithms, or AI for emulating human cognition in the interpretation, analysis and
understanding of healthcare data. For example, AI-based diagnostic algorithms applied to
mammograms are assisting in the detection of breast cancer, serving as a second opinion for
radiologists (Shiraishi et al., 2011).
AI was applied to healthcare and generated well-performed medical applications (Jiang
et al., 2017; Davenport and Kalakota, 2019). Insilico Medicine, for example, has successfully
developed AI algorithms that can stop viral reproduction (McCall, 2020). Another idea (Rong
et al., 2020) tries to protect pregnant women by giving them nutrition advice based on their
health status and algorithm projections. Another brilliant AI innovation, epileptic seizure
prediction, helped to reduce the impact of epileptic crises (Cook et al., 2013). With AI and the
development of a novel movement-detecting gadget, it was also possible to predict early
stroke with a high rate of success (Villar et al., 2015). IBM Watson is another reliable AI
system that could effectively assist in the diagnosis of cancer (Somashekhar et al., 2017).
Technological progress helped also to generate systems that simulate the functionality of the
human brain (Hassabis et al., 2017) as well as medical applications of radiosurgery (Siddique
and Chow, 2020). The medical healthcare sector got also the support of AI through the
generation of new systems and tools like artificial surgeries simulators, personal digital care,
healthcare devices and neurological disorders software detectors.
The implementation of AI has gone through various developments, due to which various
ethical issues have emerged. Some of such issues include patient safety in terms of data
(Challen et al., 2019; Choudhury and Asan, 2020), the relationship between the doctor and a
patient is at stake (Hashimoto et al., 2018), as the doctors have to rely on the black box AI
algorithm to derive the diagnosis.
To the best of our knowledge, during the last three years, only three research studies
directly focused on studying the ethical challenges associated with the introduction of AI in
healthcare. The first research work (Char et al., 2018) focused on studying the ethical
challenges associated with potential errors that algorithms may generate and how they can
impact the decision-making process. The same research work (Char et al., 2018) raised the
ethical concern of algorithms and how they could become a repository of the collective
medical mind. Another research work (Guan, 2019) studied the governmental role in AI in
protecting the ethical values associated with the emergence of AI in healthcare. The healthcare
researcher stressed the need for governments to perform ethical auditing and specify the
responsibilities of stakeholders in the ethical governance system. Another researcher (Gerke
et al., 2020) identified the legal challenges posed by AI in healthcare in the USA and Europe.
They support the idea of protecting ethical values by the force of law and through the
reinforcement of regulation.
The progress of the use of AI in healthcare and medical care is very fast. As much as we 123
recourse to AI, as much as we face more ethical challenges associated with this technological
development. Contrarily, the speed of research and the concerns of social sciences researchers
regarding the ethical challenges are not progressing as desired. This research comes to fill the
observed gap. Those ethical challenges must be identified and mitigated as long as it
threatens patient’s preference, safety and privacy. Therefore, the focus of the current study is
on examining AI and public health and the ethical associated dilemmas. This research study
aims to (1) study the concept of AI and its developments. (2) Analyze the AI usage in the
healthcare sector. (3) Assess the ethical dilemmas due to AI implications in the healthcare
sector. (4) Offer recommendations to the healthcare professionals for minimizing the
dilemmas and consider the implementation of AI, quite comprehensively.

Research methodology
A narrative review research method focusing on content analysis has been used. We have
employed a deductive approach to determine the ethical facets of adopting AI in the
healthcare sector. The current study is complemented by a review of related studies. The
secondary data have been collected from authentic resources available on the Internet. We
mainly used research through Google scholar by referring to words such as “ethical
challenges”; “Artificial intelligence” “Healthcare and medical sectors”. The first research
using these three keywords as the main components of the research titles makes it possible to
get only three manuscripts published by the end of 2020. The limited number of papers
observed pushed us to extend the search parameters by looking for these keywords
anywhere in the article. Changing the search parameters, making it possible to retain 22
interesting research. We mainly tried to catch the progress observed in terms of the use of AI
in the health sector. We also tried to look at the different ethical challenges that could be
observed by practicians, researchers and users.

Advantages of AI in the public health sector

Public health is doing its best to achieve optimal health outcomes by designing and
implementing such standards which can modify the diagnosis and treatment of diseases.
There is no doubt that AI had widespread ramifications that revolutionized the practice of
medicine, transforming the patient experience and physicians’ daily routines. The AI applied
to healthcare generated several positive outcomes (Harrison, 2018; Serag et al., 2019;
Tschandl et al., 2020; Dembrower et al., 2020; Sechopoulos et al., 2021). The diagnosis became
faster and more accurate through the application of AI tools and systems (Harrison, 2018;
Serag et al., 2019). As stressed above, today it is possible to diagnose skin cancer via an
intelligent computer program (Tschandl et al., 2020). Similarly, recently developed algorithms
could help physicians to make better diagnostics applied to mammograms (Dembrower et al.,
2020; Sechopoulos et al., 2021) in particular, and medical issues in general.
Also, researchers reported that AI applications are improving the ability of the technology
to enhance diagnosis and care accuracy. AI helps in improving the benefits of health
initiatives such as analysis of non-structured or semi-structured text from e-health reports,
TECHS online media and publications for evaluating the effect of suggestions and actions on public
1,2 health (Tucker et al., 2019). There are several AI-based models which can help in interpreting
the radiographs (Majkowska et al., 2020). Digital pathology is a more recent groundbreaking
advancement, while radiology imaging was the first to introduce digital data (Dreyer and
Geis, 2017). Most hospitals have also digitalized their health records to store their data
(Bukowski et al., 2020). AI applications, in general, have supported physicians and physicians
in the field of information systems for health, disease and condition tracking, predictive
124 support for modeling, decision-making and imagery.
Thus, we can say that the implementation of AI will prove beneficial for healthcare, as a
diagnosis will be automated, hassle-free, and will offer ease to the patients. AI will take the
healthcare sector up to the next level. IT will work as a game-changer in healthcare, as AI will
result in creating an efficient healthcare ecosystem. AI implementation will reveal the disease
risks quite early and will help us to save lives at an earlier stage. Nonetheless, one may
question if developers and practitioners of AI for healthcare applications have values that are
always aligned with the values of clinicians? Are they committed to legal requirements that
regulate the AI processes and implementation? And to what extent profit versus best patient
outcomes and safety are conflicted? Thus, there are some queries about AI practices to lay
down the ethical foundation for using its technology safely and effectively in healthcare,
including patient privacy, safety and medical education.

Ethical dilemmas in AI and public health

The use of AI in the clinical practice of healthcare has huge potential to transform it for the
better and improve the efficiency of medical diagnosis and healthcare applications in general,
but it also generated certain ethical concerns. Safety is one of the biggest challenges for AI in
healthcare. A very good example is IBM Watson for oncology (Siddique and Chow, 2020) that
uses AI algorithms to assess information from patients’ medical records and help physicians
explore cancer treatment options for their patients, which has later come under criticism by
reportedly giving unsafe and incorrect recommendations for cancer treatments. The problem
is that instead of using real patient data, the piece of software was only trained with a few
synthetic cancer cases, meaning they were only developed by doctors (Brown, 2018). AI
algorithms indeed help doctors in diagnosis and treatment by analyzing patient information
such as IBM Watson oncology to discover the best treatment options. However, there are
times when the system was unable to provide appropriate diagnoses and recommendations
(Ross and Swetlitz, 2018).
Another issue arises when the AI algorithms are trained on data of specific people; it gives
biased results (Chen et al., 2021). In this condition, AI-based systems will not be able to give
relevant and accurate results for every patient and could amplify existing societal biases
(Madaio et al., 2020). Such a situation will lead to incorrect diagnosis and treatment. AI offers
results that are biased because this is the difference between technology and humans. This
situation can be resolved by collecting a large amount of correct and relevant data. This
example raises very important questions related to the reliability and validity of the used data
in developing and implementing AI and developers’ and portioners’ transparency and biased
decisions. Indeed, any AI system or human-trained algorithm will only be as trustworthy,
effective, valid and fair as the reliable data that it is trained with as the honest and transparent
developers and practitioners.
On the other hand, rising concern regarding the risk of breach of patient privacy and
confidentiality is more than preoccupying (Verma et al., 2021; Saheb et al., 2021). For example,
the application of facial recognition technology could constitute a serious threat to the proper
informed consent, incidental findings and data security. In addition, the emergence of AI
could be associated with certain dangers that need to be legitimated and elucidated through
the update of policies, procedures, informed consent and other legal documents. The privacy AI in
of patients is considered as one of the main ethical challenges. healthcare
Healthcare organizations are at risk from data breaches because they rapidly threaten
patient trust, leading to people failing to disclose vital health information from physicians.
Landi (2022) noticed that, in 2021, the number of cyber-attacks observed in the USA attained
an all-time peak. In fact, the data collected from the US Department of Health and Human
Services admitted that 45 million individuals were affected by healthcare cyber-attacks.
Research conducted by CALYPTIX Security (2018), admitted that there are 5 main factors 125
that could lead to or motivate a health information piracy. In fact, in 33.5% of cases the loss or
non-protection of data is associated with Human Errors. Also, in Healthcare sector, the data
Misuse or use in non-preauthorized activities comes in the second place with 29.5% of cases.
The threat of physical theft comes in the third place with 16.3% of cases, followed by hacking
which counts for 14.8% of cases. Malwares are viewed as the most rarely reason with 10.8%
of cases. A concrete example of data security breach was observed in Goggin (2019) article. In
fact, in 2019, Facebook employed AI to collect data from users’ postings and then forecast
their mental health and proclivity for suicide. As a result, Facebook collected and kept users’
mental health information without their knowledge or consent. Another case health records
threat was reported by the journalist Alder (2020). Accordingly, the stage data, personal and
health information of more than 2.5 million US patients was published online by an AI firm
named Cense AI. The data were openly accessible through the internet and required no
credentials to retrieve. These data had been temporarily stored into a storage repository
before becoming deposited into the AI system, according to the author.
Most of the time, AI-based systems solutions can violate the privacy rights of the patients.
AI-based applications raise some concerns about user agreements. A contract that a person
agrees to without a face-to-face dialog is contrary to the generally informed process of consent
(Klugman et al., 2018). Most of the time, people do not take time and regularly violate user
agreements (Friedman et al., 2000). Accordingly, some concerns may arise as to what kind of
data should be gathered by AI developers and practitioners? For what purposes patients’ data
can be processed, used and shared? Can patients have the right to withdraw their data? What
about the vulnerability to both cyber and physical threats and hazards? Indeed, in the world of
big data, it is of pivotal importance that there are data protection laws in place that adequately
protect the privacy of individuals and patients (Pavlova et al., 2019). Regarding the patient’s
privacy, Voigt and Von dem Bussche (2017) reminded us that we live in a knowledge society
characterized by a continuous accelerating pace of technical progress. Consequently, there is an
urgent need to protect patients’ data from technological progress. Thus, Voigt and Von dem
Bussche (2017) highlight the importance of the reference to the concepts of Privacy by Design
and Privacy by Default. The concept of privacy protection by design consists of limiting the
data collected to the strict necessary minimal level. The concept of privacy protection by default
is referred to in the Art. 25 Sec. 2 of General Data Protection Regulation (GDPR) is also useful
once we refer to extending the use of AI. By relying on this concept, the data processor will only
and strictly have access to personal data that is necessary to execute the purpose of his mission
(Voigt and Von dem Bussche, 2017). Researchers and AI specialists should consequently be
aware of these principles to better protect the patient’s privacy.
The emergence of AI in the healthcare sector will lower the level of Human errors, will also
lower the level of Human legal responsibility. However, legally speaking, who is going to be
accountable and responsible for the diagnosis of errors that could potentially make AI
through machines? Consequently, another serious ethical dilemma emerges. Machine
learning-based clinical guidance leads to the introduction of third-party actors thus
challenging dynamics of responsibility in patient–doctor relationships.
A more comprehensive model to sum up the AI ethical dilemmas and benefits are
mentioned in Table 1.
TECHS Risk priority number (RPN)
1,2 Occurrence Severity Detactability
Benefits Risks (O) (S) (D) RPN

Improving efficiencies for The fast pace of 10 10 6 600

the operational technology and impact
management of healthcare on decision-making
126 businesses processes
Accuracy of diagnosis and Moral hazard and 5 10 5 250
treatment in personal human intervention
medicine
Table 1. Increased insights to Lack of regulation and 10 10 9 900
RPN, benefits and risks enhance cohort treatment algorithm bias
associated with the Privacy pressures 10 10 10 1,000
application of AI in the Safety 10 10 10
healthcare sector Human errors 5 10 10 500

Discussion and recommendations

This research aimed to (1) study the concept of AI and its developments. Through this
research, we were able to report the technological progress observed in terms of the use of AI
in the health and medical fields. The research conducted has even enunciated and unfolded
the ethical dilemmas that are likely to be raised when AI will be introduced in healthcare.
The actual research aimed also to (2) Analyze the AI usage in the healthcare sector. The
findings confirm that safety is one of the most important ethical dilemmas being faced.
Challenging dynamics of responsibility in the patient-doctor relationship is also one of the
prime ethical dilemmas being encountered. AI is capable of mimicking the overall intellectual
process of humans, which increases its credibility and also offers harm to humans. It was also
found that patient safety is the most crucial ethical dilemma, as, at the end of the day, AI is a
new technology and technology can lead to failure. The findings are in accordance with the
literature as Klugman et al. (2018) averred, concerns about user agreements and hacking of the
patient’s personal data impose a huge influence on the overall introduction of AI in healthcare.
Finally, we agree with Majkowska et al. (2020), that AI, on the whole, will increase the
performance of the healthcare sector. However, we need to address some recommendations to
mitigate the ethical potential issues that we could observe through the use of AI.
The third research objective consists of (3) assessing the ethical dilemmas due to AI
implications in the healthcare sector. The researcher was able to conclude that the potential
harms and risks associated with AI application need also to be transparent to avoid any
potential ethical issues. Today, we need to evaluate and assess the organizational and legal
progress associated with the emergence of AI in the healthcare sector. To avoid any ethical
dilemmas in the application of AI, it becomes important to verify that these new tools and
systems are ethically applied. We must also be certain that medical practitioners are well
covered and protected regarding the different secondary effects of this artificial medical
progress. In addition, it becomes important to guarantee the safety and security of patients
and protect their rights even if that one day they will be applied to deal with systems and not
physicians.
The last objective (4) consists of offering recommendations to the healthcare professionals
to minimize the dilemmas and consider the implementation of AI, quite comprehensively.
Previous research conducted by Wolff (2021) suggested 3 key success factors of AI in
healthcare sector. The first factor recommends the setting of a risk adjusted policy frame that
clarifies the limits of actions in term of precautions, permissions, accountability, liability and
culpability. The second success criteria suggest maintaining a centralized technology
architecture that allows for practical and lawful metadata. Finally, Wolff (2021) highlights the AI in
necessity of having key performance indicators (KPI) that measure the medical and economic healthcare
impact of AI in healthcare. Building on that, we suggest 4 key success factors of AI in
healthcare sector that could potentially reduce the impact of the observed ethical dilemmas.
Patient safety and privacy remain paramount, therefore, the development of a regulated way
(1) by establishing a partnership between computer scientists and clinicians should be
considered to implement AI effectively. Here, the focus needs to go through adopting
appropriate laws, regulations and policies that organize and regulate the use of AI in 127
healthcare. Such measures will also protect rights and specify duties of users and patients.
Secondly, training (2) healthcare staff members, nurses and doctors toward AI will lead to a
far more comprehensive introduction of AI. Thirdly, we recommend that the teaching of
ethics, AI ethics, and AI courses (3), become mandatory in all IT, medical and paramedical
training programs. Finally, we need to mention that AI, however, should be implemented to
reduce the health inequality (4) not increase, from all aspects geographically, economically
and socially.

Conclusion
Technological advancement is surely beneficial for our environment and society, but it is
important to remember that the misuse of these techniques can lead to the downfall of
humanity. Today, we become more aware about the importance and potential of AI in
transforming healthcare services delivery. As of today, we observed several AI technology-
based developments that completely innovated and transformed the organization and
delivery of healthcare services and practice. The continuous and effective use of AI in
healthcare will increase its clinical and operational efficiency. Also, the continuous
investment in the research and development of AI algorithms and machine learning will
continually sustain the accuracy of AI predictions and optimize levels of data privacy use and
protection and network security in this context. Thus, for making the best use of these
advancements, it is important to control its growth.
Governments and policymakers need to better control this growth by implementing laws
and regulations that regulate and limit the frontiers of development. Researchers need to be
more sensitized about the importance of the respect of ethical values as well as the importance
of technological progress. Organizations that work toward technological advancements must
work toward quality control. Keeping control of their system and marinating their systems
will help them produce technology that is safe and healthy for the environment, people and
the planet. The research stressed that ultimately, patients will still be treated by physicians
no matter how much AI changes the delivery and quality of healthcare, and there will always
be a human element in the practice of medicine, however, AI will remain as a technological
tool that should benefit the healthcare, however, with more ethical foundation. The paper also
revealed the existence of some ethical dilemmas that are associated with the use of AI in the
medical and healthcare fields. Privacy of patients and data protection, safety, as well as
patient-doctor relationships are amongst the most highlighted ones. Technological
advancement is expected to affect almost all aspects of human life, especially the way
humans work. The advancement will have an equal impact on both the overall functionality
due to ethical dilemmas raised.
It is concluded that the impact of ethical dilemmas can be minimized but cannot be
eliminated in full if AI is introduced in healthcare. Every technology has its pros and cons as
well as concerns, therefore, no one can eliminate the ethical issues associated with the use of
AI in healthcare. However, these ethical issues can be minimized by continuous monitoring.
A balance between AI and the human workforce will also somehow help to solve ethical
dilemmas. Only complex tasks and diagnoses need to be done by AI, not everything.
TECHS AI will only see what it is programmed to see whereas an experienced medical professional
1,2 will see and diagnose what is not apparent.

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Corresponding author
Chokri Kooli can be contacted at: ibm4chk@yahoo.fr

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