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A conceptual framework
for Artificial Intelligence of
Medical Things (AIoMT) 8
Hamed Nozari
1
, Reza Tavakkoli-Moghaddam
1
, Javid Ghahremani-Nahr
2
,
Esmaeil Najafi
3
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran;
2
Faculty Member of Academic Center for Education, Culture and Research (ACECR), Tabriz, Iran;
3
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University,
Tehran, Iran
8.1 Introduction
The Internet of Things (IoT) devices, which can report in a real-time without human
intervention, has provided a new level of efficiency, convenience, and automation in
medicine and telemedicine. For example, healthcare is one of the front lines of the
IoT business. The growth of the IoT in various fields has brought astonishing results
in the achievements and progress of mankind in the 21st century (Nozari, Fallah,
Kazemipoor et al., 2021). Although the concept of the IoT is often seen alongside
the construction, warehousing, and smart factories of the future, IoT applications
in healthcare are another lesser-known area among interconnected object enthusi-
asts. Using the IoT can benefit physicians, patients, and the general public because
the accuracy of measurement and speed of operation of intelligent systems and com-
puters is thousands of times higher than humans. Applying technology to medicine
can significantly help reduce treatment costs and mortality rates (Nozari et al.,
2021). The Internet of Medical Things (IoMT) is a collection of medical devices
that connect to the internal network of internal health systems and physicians via
the International Internet of Things. Through IoMT, a person’s health data is stored
in a database on the internet and made available to the relevant organization or in-
dividuals, such as physicians, nurses, or the World Health Organization (Jain et al.,
2021).
On the other hand, artificial intelligence (AI) is used in different layers of the IoT.
It can be seen at varying levels of this technology; however, one of the main func-
tions of AI in the IoT is on information collected from IoT equipment. Large vol-
umes with the concept of big data require precise processing to obtain acceptable
results, for which AI is used and helps in processing (Alhayani et al., 2021). An
AI system can analyze data and make decisions when needed. Therefore, objects
that connect to the internet must also have artificial intelligence. Thus, the combina-
tion of this technology can play a significant role in increasing the efficiency and
effectiveness of services and activities of healthcare systems. In this chapter, a
CHAPTER
175
Computational Intelligence for Medical Internet of Things (MIoT) Applications
https://doi.org/10.1016/B978-0-323-99421-7.00007-6
Copyright ©2023 Elsevier Inc. All rights reserved.
concept called Artificial Intelligence of Medical Things (AIoMT) is defined and
introduced to show the capabilities and functions of this technology in health sys-
tems (Sun et al., 2020). The data collected by millions of IoT devices in medical pro-
cedures is so large that it is difficult to separate and extract useful information. To
organize the unstructured data into a meaningful data set, artificial intelligence-
based algorithms are used to remove useless data and maximize the use of the
data in therapeutic processes (Nozari, Fallah, Szmelter-Jarosz, &Krzemi
nski,
2021).
This research has attempted to provide a conceptual framework for an AI-based
health system that combines a powerful AI computing system with powerful IoT
technology as one of the most important sources of big data generation. For this pur-
pose, using literature review, the most important components and effective parts in a
health system were extracted. Then using the opinions of experts in the field of treat-
ment and IT professionals familiar with the concepts of artificial intelligence and
IoT causal relationship, the defects of these components were identified with tech-
nologies. Finally, the framework’s validity was confirmed using the opinion of ex-
perts active in these fields. Using this framework, we can identify the essential
elements in implementing a health system, emphasizing big data and the use of
transformative computing technologies, and taking steps to create an intelligent
treatment structure.
8.2 IoT in healthcare
The concept of the IoT requires the use of electronic tools to record information to
connect to the Internet, Bluetooth, or other networks, the ability to perform specific
tasks (e.g., sending to the server and processing) to the system. The use of many in-
dustrial products and equipment can change if the internet can be used because there
are a lot of changes in the field of healthcare every year. New technologies improve
patient care methods and make physicians’ treatments more efficient. The field of
medicine has great potential for using IoT. The integration of IoT with other medical
devices has played an essential role in the development of the medical field of coun-
tries. In general, the IoT has significantly reduced treatment costs and improved
treatment outcomes by providing tools for integrating treatment and medical sys-
tems and increasing their accuracy and efficiency. Patients and healthcare providers
can also benefit from the use of the IoT in health. Some IoT applications can be
found in mobile health software or devices that record personal health information.
Many hospitals also use this technology in medical equipment, staff, and patients
(Maksimovi
c et al., 2015). The main reason for using this technology is to reduce
the human role and avoid human errors that lead to the provision of services in
health. This technology can provide medical services to patients by remote access
and control and facilities to record patient information and transfer them for review.
The IoT can be used in various medical fields, including emergency alert systems,
remote patient care systems, chronic illnesses, fitness programs, and geriatric
176 CHAPTER 8 A conceptual framework for Artificial Intelligence
care. These include heart rate monitoring systems, health monitoring systems, blood
pressure monitoring systems, hearing aids, and artificial pacemakers. In more
advanced cases, the devices monitor the course of treatment and medications and
their amount. Also, there are programs on the IoT that allow physicians to monitor
their patients after they are discharged from the hospital (Yuehong et al., 2016). One
of the most important uses of the IoT in the health sector is to monitor vital signs and
specific parameters of people with chronic and common diseases. Common diseases
include heart disease, respiratory diseases, and diabetes. To reduce costs and in-
crease patient care, the use of smart health devices is increasing day by day (Yehia
et al., 2015). Fig. 8.1 shows how the Internet of Things affects the healthcare
industry.
IoT-enabled smart and connected solutions such as smart sensors, wearables, and
smart health monitoring systems are being used to unleash the potential growth of
the healthcare industry. They will do this by improving treatment using effective
health tracking. The growing popularity of the Internet of Things in the field of
healthcare and medicine has been enhanced by modern techniques such as IoMT.
IoMT is an ecosystem of smart devices that can communicate in a real-time environ-
ment and formulate results. This greatly reduces human error and eliminates many
decision delays (Baker et al., 2017).
Digital healthc are systems use IoT and big data to bring seamless digital commu-
nication with the patient. These systems are also increasingly connecting to the
FIGURE 8.1
How IoT is Transforming the Healthcare Industry (Shrimali, 2020).
8.2 IoT in healthcare 177
internet through various wearable medical technologies to assist us in real-time pa-
tient information.
The desire of people to live daily life without worries and assurance of supervi-
sion, as well as the unwillingness to spend long days in the hospital and even the
willingness of some patients to live in small towns or villages without advanced
medical facilities are also factors in using smart health tools (Shrimali, 2020).
Typically, the IoT solution in health systems includes the following functions:
• Collecting vital signs continuously: Vital signs, including heart rate, blood
pressure, body temperature, and respiration are collected and stored all day and
night using intelligent tools equipped with a communication system.
• Collect vital signs periodically: Vital signs, including heart rate, blood pressure,
body temperature, and respiration, are collected and regularly stored at
adjustable times at all hours of the day and night using intelligent tools equipped
with a communication system.
• Collect specific parameters related to chronic and common diseases continu-
ously: Specific parameters about chronic and common conditions such as blood
sugar, blood lipids, body water percentage, stress level, and seizure probability
using intelligent tools equipped with a continuous communication system
collected and stored at all hours of the day.
• Collection of specific parameters related to chronic and common diseases peri-
odically: Specific parameters related to chronic and common conditions such as
blood sugar, blood lipids, body water percentage, stress level, and seizure
probability using intelligent tools equipped with periodic communication sys-
tem and are collected and stored at adjustable times at all hours of the day.
• Tracking and monitoring: All objects and their communication capacities with
wireless sensor network devices can be measured 24 h a day, 7 days a week and
are tracked and monitored by the identification screen that exists in all places
and has a high communication capacity.
• Remote services: health and life services (e.g., emergency and first aid, education
and health of residence, diet and medicine management, telemedicine and
remote diagnosis, and health social networks) can be remotely provided via the
internet and optimal devices.
• Sending intelligent content to the user: The system has the ability to according to
the vital signs and specific parameters related to chronic. Common diseases
collected by each person, and according to the threshold levels defined for them,
if the symptoms of the disease appear, the user page modifies the person
accordingly and automatically posts articles or educational videos related to the
disease on the user’s page.
• Interorganizational integration: With the help of IoT an integrated interorgani-
zational information system can be achieved. This feature allows authorized
persons (i.e., physicians, nurses, radiologists, and physiotherapists) to access all
patients’ medical information in different locations (i.e., hospitals and doctors’
offices).
178 CHAPTER 8 A conceptual framework for Artificial Intelligence
Fig. 8.2 shows how this revolution in medicine manifests itself in practice in or-
dinary IoT hospitals. The patient will have an ID card that, when scanned, will be
connected to a secure cloud that stores essential e-health information, laboratory re-
sults, medical history, and prescription.
The IoT in most healthcare systems is designed to enter, store, receive, and ex-
change health information. The system increases the number of devices and in-
creases the mobility of data to support health professionals in their consultations.
In addition to the benefits of using the IoT in hospitals, reliability, several challenges
related to availability, scalability of management, mobility, performance, security,
interoperability, and privacy should be taking into consideration during its
application.
The IoT helps monitor equipment status in hospital wards and track patients’
health with special sensors. Such devices can monitor patients’ health status from
the moment they enter the hospital, collecting and updating information about
them without a nurse (thus saving time and money). One of the essential features
of IoT medical solutions is the remote provision of some medical services and health
monitoring. This concept means replacing the medical staff with intelligent pro-
grams in different devices. For example, special flexible devices are used to rehabil-
itate strokes. They are attached to the neck to track swallowing and speech disorders.
Clinics with intelligent systems can provide more straightforward and faster solu-
tions to the care process (Nozari, Szmelter-Jarosz, &Ghahremani-Nahr, 2021).
FIGURE 8.2
Hospital IoT scenario (Dauwed &Meri, 2019).
8.2 IoT in healthcare 179
IoT devices in medicine reduce doctors’ errors in diagnosis and help monitor the
work of a medical center. Such tools can analyze the information obtained and pro-
vide solutions that also contribute to employee efficiency. With the advent of the
medical IoT, healthcare is now finding completely new ways to monitor and treat
health. The world can now use smart drugs with sensors inside: they allow doctors
to learn more about a patient’s health.
According to forecasts and analyzes, healthcare is the most important part of the
Internet of Things. Intelligent devices and systems are not created to replace doctors
and nurses, but to help improve their work. The main driver of the growth of the IoT
is the need for tools and programs to maintain health.
8.3 Big data in healthcare
As mentioned in the previous section, the IoT is one of the most important sources of
big data production. Through this big data and calculations on this data, we can use
these revolutionary technologies to serve patients. win. For this reason, in this sec-
tion we will describe some of the most important effects of big data on treatment
systems.
The use of big data analysis in healthcare has positive results and life-saving. Big
data refers to the vast amounts of information generated by the digitization of every-
thing that is synthesized and analyzed by specific technologies. Here, big data uses
health services to use specific population health data (or a specific individual) and
potentially help prevent disease pandemics, treat diseases, reduce costs, and more.
Now, treatment models have changed, and many of these changes are driven by
data. Doctors want to know as much as possible about a patient and his life as
soon as possible so that they can recognize the warning signs of a serious illness
when it occurs (Nahr et al., 2021). It is much easier and cheaper to treat any disease
in the early stages. By analyzing healthcare data, prevention is better than cure, and
management allows insurance to provide a more appropriate cost package to paint a
comprehensive picture of a patient. In fact, collecting large amounts of data for med-
ical purposes has been costly and time-consuming for many years. With today’s
ever-improving technologies, not only does the collection of such data become rele-
vant insights, but it can also be used to provide better care. Healthcare data analysis
aims to use data-driven findings to predict and solve problems before it is too late.
This speeds up the evaluation of treatment methods and therefore leads to a better
follow-up of individual health (Mokliakova &Srivastava, 2022).
Physicians’ decisions are increasingly evidence-based, meaning they rely on a
wealth of research and clinical data. Like many other industries, as data grows, so
does data management, and experts need help. This new therapeutic approach means
that there is more demand for big data analysis in medical centers than ever before.
Using big data in healthcare allows strategic plans to better understand what peo-
ple think. Care managers can analyze the survey results among different population
groups and consider the determining factors to prevent the use of treatment. In the
180 CHAPTER 8 A conceptual framework for Artificial Intelligence
field of health, big data covers a wide range of information, including physiological,
behavioral, molecular, clinical, medical imaging, disease management, medication
history, nutrition, or exercise parameters.
Data sources can be categorized as follows:
1. Traditional medical records: including electronic medical records (EMRs),
electronic health records (EHRs), medical history, and laboratory reports that
help better understand disease outcomes and optimize healthcare delivery.
2. Genomic, microbiomic, proteomic, and metabolic data used to understand dis-
ease mechanisms and accelerate the personalization of medical therapies.
3. Social media data, biometric data (wearable systems and sensors) that provide
information about people’s behavior and lifestyle (Kashani et al., 2021).
Although health data provide potential value for optimizing care, they are still
considered a by-product of healthcare. Since this electronic information is not
widely used and wasted, it is essential that raw data be converted into meaningful
and practical information.
Despite the many potential benefits of big data analytics, the health industry is in
the early stages of using this technology. Despite the large amount of data available,
lack of knowledge, lack of infrastructure, and the need for massive initial investment
make using big data more difficult. The challenge of storing large amounts of data
can be tackled using cloud computing. This feature solves cost and data storage
problems, especially for small hospitals and care organizations. Another problem
is the weakness of data governance, which imposes huge costs on IT investment
for healthcare organizations. With proper data management and monitoring, the
entire company’s data sources can create business value. By developing a culture
of information sharing and data aggregation, it is possible to create interoperability
and effectively use big data analysis and forecasting capabilities. Fig. 8.3 shows the
relationship between big data analysis as well as the Internet of Things.
8.4 Artificial intelligence in healthcare
Artificial intelligence is used in many industries, but its impact is certainly critical in
medical science, where many deaths occur based on human error. This transforma-
tive technology has revolutionized medicine. With this technology, while receiving
positive treatment results, it is possible to see a reduction in costs. Artificial intelli-
gence can be considered a relatively new technology that is in its infancy in the med-
ical industry. As AI tools and machine learning become more sophisticated, artificial
intelligence in medicine is becoming more widespread. Of course, as we said, we are
still the first way and different companies are trying to find a specific strategy for it.
Due to the complexity of medical decisions, information systems to support these
decisions have increased. In the meantime, the role of intelligent systems in assisting
physicians is prominent. AI is used for a variety of therapeutic and research pur-
poses, including diagnosis, management of chronic diseases, medical services,
8.4 Artificial intelligence in healthcare 181
and drug discovery. AI technology has better patient-care potential, administrative
processes of pharmaceutical organizations, and the ability to diagnose disease
than humans (Jiang et al., 2017). AI health programs provide the conditions for peo-
ple to assess their symptoms and take care of themselves if possible. AI systems
enhance the quality of life of individuals. Hospitals look for AI solutions to increase
cost savings, improve patient satisfaction, and meet staffing and workforce needs.
As the digital world continues to grow in the field of healthcare, more and more
data are being generated and stored in this space every day (Reddy et al., 2019).
The amount of data available is growing at a staggering rate, and the volume of
this data is multiplying almost every two years. In other words, the amount of infor-
mation that we have today in healthcare is considerable, and AI can quickly classify
and organize this huge mass of data (Scho
¨nberger, 2019).
Existing AI solutions are divided into six main areas in which AI directly impacts
the patient and three areas of the healthcare value chain that they can benefit from.
Fig. 8.4 shows these key areas.
The potential of AI for the use in the field of medicine is extremely high. Thus,
AI can help simplify drug production processes to quickly provide appropriate treat-
ments for new and old diseases. This system can even help identify previously un-
known diseases. AI can increasingly be used in the following cases:
• It helps doctors act faster to treat serious illnesses, thus reducing mortality in
hospitals worldwide.
• Provides a quick and accurate diagnosis that is virtually impossible using human
intelligence alone.
• Mistakes made as a result will limit human fatigue.
FIGURE 8.3
IoT big data processing.
182 CHAPTER 8 A conceptual framework for Artificial Intelligence
However, the important thing is that AI has not been offered to replace doctors in
healthcare. Instead, AI is practically provided to enhance the capabilities of medical
professionals and can bridge the gap in their abilities. Also, although AI technology
can have many benefits for the healthcare industry, finding a way to implement this
new technology in the current healthcare system and finding training solutions for
medical professionals can always be a significant challenge (Bohr &Memarzadeh,
2020).
Given the above, it can be seen that AI is used for all three classic medical tasks:
diagnosis, prognosis, and treatment, but more in the field of medical diagnosis. In
general, the medical diagnosis cycle includes observing and examining the patient,
collecting the patient data, interpreting the data using the physician’s knowledge and
experience, and then formulating the diagnosis and treatment plan by the physician.
The diagnostic treatment cycle is shown in Fig. 8.5.
All AI systems used in medical engineering and medicine use deep learning al-
gorithms to diagnose the disease. Data from healthcare centers are used to train these
systems. Then, these AI systems employ specific techniques to achieve the diag-
nosis. There is a challenge and problem in deep learning techniques is transparency.
The inputs and outputs given to the system are transparent; however, it is not clear
how AI can diagnose the disease. Another point in this regard is that the quality of
the AI system in detection depends directly on the quality of the data (Szolovits
et al., 1988).
Artificial intelligence applications have helped to increase the speed and accu-
racy of medical diagnosis. Artificial intelligence is used in the research and devel-
opment of new medical products. It also helps automate repetitive tasks of
medical staff such as routine office work, scheduling and scheduling to improve ef-
ficiency and reduce costs. Artificial intelligence has also helped physicians analyze
historical data to identify insights for better treatment (Li et al., 2020).
FIGURE 8.4
Six key areas of artificial intelligence solutions in health systems.
8.4 Artificial intelligence in healthcare 183
Machine learning, and more specifically deep learning algorithms, have recently
made great strides in medicine. These algorithms automatically detect diseases and
make the process cheaper and more accessible. However, how do machines learn to
diagnose disease? Machine learning algorithms can learn to see disease patterns like
doctors. The main difference is that these algorithms require thousands of intercon-
nected examples to learn. And these examples need to be cleaned and digitized;
machines do not know how to read from the lines of books! Therefore, machine
learning is particularly useful in cases where physician test information is stored
in a database. This data is processed in such a way that it can be used as a result,
which is the input of another type of system that provides the proposed differential.
It provides input, diagnosis, and conclusion based on a set of rules. A tree-like hier-
archy is provided using data that these systems can help with. These systems are
based on the “expert systems” rule. Systems experts consider this experience to
make the final decision. Most of these systems are able to answer the questions
with “what” instead of “how” and at the same time explain the reasoning behind
them (Fallah et al., 2021).
The increasing availability of electronic data in the field of healthcare has pro-
vided an important opportunity to discover and apply various methods in improving
healthcare. While artificial intelligence can and often is well used, it can also detect
and exploit vulnerabilities. Machine learning has the power to be used for both good
and, unfortunately bad purposes. As more connected medical devices are built on
artificial intelligence, the risks of cybersecurity increase, and it is even more impor-
tant for manufacturers to use advanced security protections in the design phase to
ensure the safety of healthcare organizations, providers, and patients.
FIGURE 8.5
Diagnostic-therapeutic cycle of medicine (Mokliakova &Srivastava, 2022).
184 CHAPTER 8 A conceptual framework for Artificial Intelligence
8.5 Artificial Intelligence of Medical Things (AIoMT)
There have been powerful combinations throughout history, each with unique char-
acteristics, but miracles happened when they were put together. Similarly, the com-
plete combination of the IoT with AI, known as AIoT, allows companies to take
advantage of both (Nozari &Sadeghi, 2021). AIoT means using the IoT to perform
intelligent tasks with the help of AI integration. AIoT helps connect IoT devices to
sensors with AI capabilities, which is done without human intervention. AIoT has
the following features (Kishor &Chakraborty, 2021):
• AIoT is the brain that controls the nervous system to make better business
decisions.
• AIoT that provides intelligent decisions requires software code written by pro-
grammers to carried out various tasks.
• AIoT is the next generation of the IoT, and its primary design goal is independent
operation without human support using predictive maintenance and AI
algorithms.
• In AIoT, IoT self-correction devices are created by analyzing data for better
decision-making.
With excellent personalization experience, active intervention, and intelligent
automation, AIoT offers countless benefits to companies and consumers. The bene-
fits of AIoT are shown in Fig. 8.6.
Maintaining health is a difficult challenge and takes time. In today’s world, with
all kinds of challenges, everyone is very cautious about their health, and technolo-
gies (e.g., IoT and AI) use the entire healthcare sector. Frequent visits to doctors for
regular checkups are difficult for a large population. This problem is solved with
FIGURE 8.6
Benefits of AI of things (AIoT).
8.5 Artificial Intelligence of Medical Things (AIoMT) 185
wearable devices (e.g., smartwatches, fitness trackers, panic buttons to track blood
sugar levels, and cholesterol levels). The healthcare industry has to deal with vast
data, and the IoT is adding significant volume to wearable devices. AI provides
meaningful insights into data and helps with real-time responses, inventory manage-
ment, human resource management, and affiliate pharmaceutical services. AIoT ap-
plications help to collect data to provide preventive measures for the individual,
early diagnosis, and medication administration (Reddy, 2018). Fig. 8.7 provides a
framework for giving relationships between the parameters affecting AIoT on the
healthcare system, called Artificial Intelligence of Medical Things (AIoMT).
In order to create a conceptual framework for expressing the connections be-
tween transformational tools such as the Internet of Things and artificial intelligence
FIGURE 8.7
Framework of the proposed system (AIoMT).
186 CHAPTER 8 A conceptual framework for Artificial Intelligence
and the big impact of big data from IoT technology on the industry, all the basic parts
of the field were separated using the opinions of experts. Then, by reviewing the
literature, the key elements in each section were extracted and confirmed by experts
active in Jose Medical Services and familiar with information technology and trans-
formational technologies. Then the causal relationship between the elements was
identified and put together in a conceptual framework. Active experts in related
fields also approved this framework.
This framework shows the clear relationships between the effective parameters
in the treatment systems based on AI of objects. Using this framework, significant
points can be identified in specific situations and increasing efficiency can be an
optimal effort, which is one of the main tasks of IoT-based systems.
8.6 Conclusion
There are many changes in the field of healthcare every year. New technologies
improve patient care methods and make physicians’ treatments more efficient.
The field of medicine has great potential for using IoT. The integration of the IoT
with other medical devices has been able to play an essential role in the development
of the medical field of countries. On the other hand, AI has various applications in
treatment and medicine. The meaning of these users is that everyone is among the
users of AI in the medical field, from recognizing the connection between genetic
codes to using AI robots for complicated surgeries. With all these applications, AI
has created a modern course in health services and taken it to another level.
When these technologies are combined, they reduce costs and activate the next level
of automation and productivity. As consumers, businesses, and governments begin
to use the IoT in various environments, our world will change dramatically, allowing
all of us to make better choices. This process is rapidly changing everything in the
healthcare chain, and the IoT with AI capability can also transform the healthcare
industry with smart solutions. This combination creates a concept called Artificial
intelligence of Things or AIoT. Due to the very high efficiency of this concept,
this chapter provides a framework for implementing an AIoT-based treatment sys-
tem or AIoMT. Using this framework, we can understand the factors affecting
creating an intelligent structure of the medical system, emphasizing AI and the IoT.
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