Book Volume 5
Preface
Page: i-iv (4)
Author: Shreyas Suresh Rao, Steven Lawrence Fernandes, Chandra Singh, Rathishchandra R. Gatti, Harisha A. and Rohanchandra R. Gatty
DOI: 10.2174/9789815196054123050001
PDF Price: $15
Acknowledgements
Page: v-v (1)
Author: Shreyas Suresh Rao, Steven Lawrence Fernandes, Chandra Singh, Rathishchandra R. Gatti, Harisha A. and Rohanchandra R. Gatty
DOI: 10.2174/9789815196054123050002
PDF Price: $15
A Survey on Semantic AIOT Concepts and Applications in Healthcare
Page: 1-27 (27)
Author: Sapna R., Pravinth Raja, Vidhya Banu, B. N. Shwetha and Shreyas Suresh Rao*
DOI: 10.2174/9789815196054123050004
PDF Price: $15
Abstract
The incorporation of semantics and the necessary interoperability within
these aspects is essential for the domain's proper operation as well as execution.
Healthcare systems have become an ideal arena of IoT because they tackle the
problems of humanity, especially of an older population whilst providing secure and
high-quality home care and support. The use of IoT technologies in healthcare will
improve the quality of human life, chronic illness monitoring, hazard detection, and
life-saving measures. To get more useful information from biomedical big data, it must
have interoperability. In the latest times, an increasing count of organizations and
businesses have expressed interest in combining semantic web technologies alongside
healthcare big data to transform data into knowledge and understanding. Even though
we can see a systematic acceptance of semantic technologies-based applications in the
IoT domain and across the Internet, the cumulative actual implementations are
insufficient to provide real-world rooted standards and guidelines to follow. This sets
the stage for this work, which attempts to describe current developments in the
application of semantic technologies in the IoT domain. This motivates the authors to
examine and highlight some of the developing developments in semantic technology,
its effects in the IoT area, and how they are together seen in the health-care. Over the
last several times, there has been a lot of emphasis on using SWT to enhance the uptake
of sensor networks, IoT, and WoT. Indeed, to tackle semantic interoperability and other
issues in health care domains, there is a need to comprehend its means of construction.
IoT Based Sleeping Disorder Recognition System for Cognitive Impairment Diseases
Page: 28-52 (25)
Author: Nupur Choudhury*, Rupesh Mandal and Jyoti Kumar Barman
DOI: 10.2174/9789815196054123050005
PDF Price: $15
Abstract
In the present scenario, healthcare has made significant progress with the
assistance of smart devices involving effective sensors and Internet of Things devices.
In context to this, the combination of IoT and cloud architectures are rigorously
exploited in order to process the large amount of data that would be generated by the
wearable sensor networks in near real-time applications by making use of Artificial
Intelligence supporting smart healthcare systems. In the current scenario of
globalization, in addition to the increased facilities, a wide variety of other challenges
are worked upon for providing quality and efficient healthcare benefits and facilities by
making use of cost-effective instruments and world class technologies.
An important factor for the physical and mental health of a human being, the
performance throughout the day as well as safety is the sleep quality. Effective quality
of sleep can help avoid the risk of mental depression and chronic diseases. Sleep
promotes the brain to actively get associated with the activity that is being performed
and helps in preventing various accidents that might be caused due to falling asleep.
For the analysis of the sleep quality, a continuous monitoring system is necessary
which generates effective results. With the aid of rapid improvisation of mobile and
sensor technology as well as the emerging trends of Internet of things technology, there
is a good opportunity of development of a reliable and effective sleep quality
monitoring system. This chapter effectively describes the background and applicability
of Internet of things for such systems involved in sleep monitoring. The study begins
with the review of the quality of sleep, the importance related to the monitoring of
sleep quality, the employability of Internet of things in this and its relevant field, as
well as the open issues and challenges in this and its related fields.
The IoT technology supports the preamble which would promote a cost effective and
consistent system in order to monitor the quality of sleep-in individuals. There are
several existing systems for the same purpose which involves a large amount of cost
and are cumbersome to implement. To overcome the same issue, the chapter narrates
an inventive system for monitoring and analyzing sleep patterns by making use of effective parameters. In this domain, a combination of clinical medicine, bioengineering,
neuroscience, epidemiology, mHealth, Computer Science, as well as Human Computer
interactio,n in order to approach the challenge of digitization of sleep from a
multidisciplinary perspective. This chapter describes the state of art technologies
involved in sleep monitoring and discusses the challenges and opportunities involved
from the initial step of acquiring the data to the applicability of the acquired data based
on the consumer level and clinical settings.
Recent Trends in Smart Health Care: Past, Present and Future
Page: 53-66 (14)
Author: S. Kannadhasan*, R. Nagarajan, R. Banupriya and Kanagaraj Venusamy
DOI: 10.2174/9789815196054123050006
PDF Price: $15
Abstract
Electronic gadgets, actuators, sensors, and software link every element of an
active network. The Internet of Things is the name of this network (IoT). AI
technology may help networks, sensors, and users create a large quantity of data by
assisting in the collection of data and the development of applications. The
combination of AI with IoT may advance fields including public safety, education,
healthcare, energy, transportation, and other value-added services. Smart health care
makes extensive use of the Internet of Things (IoT), notably in the areas of emergency
services, intelligent computing, sensors, security, and remote monitoring. Data privacy,
integrity, and freshness are just a few of the security issues that must be resolved in a
smart hospital. Additionally, there are privacy risks for patients, data eavesdropping,
data integrity, and unique identification. IoT technology may be used to monitor a
patient's health as well as their data. A patient's status might be tracked remotely and in
real time using the internet and other technologies. Additionally, it enables the early
identification and treatment of diseases that pose a danger to life. Medical records may
be gathered and statistical information on a patient's condition may be provided via an
IoT-enabled gadget.
With the correct software, huge amounts of data may be handled quickly and without
errors. As a consequence of these advancements, which strive to fulfil patients' unique
requirements while simultaneously enhancing treatment effectiveness, modern
medicine is on the verge of a renaissance. The essential technologies that underpin
smart healthcare are briefly described, together with the successes and challenges they
have faced, the current status of these technologies in important medical areas, and the
possibilities for the future of smart healthcare. Sensors gather data, which is subsequently sent over the internet of things (IoT) to supercomputers and cloud computing for
processing and analysis.
A Monitoring System for the Recognition of Sleeping Disorders in Patients with Cognitive Impairment
Page: 67-84 (18)
Author: Priya Dev* and Abhishek Pathak
DOI: 10.2174/9789815196054123050007
PDF Price: $15
Abstract
Sleep is one of the most important biological processes acknowledged as a
vital determinant of human performance and health. Sleep has been acknowledged to
promote healing, restore energy, improve the immune system through interactions, and
affect human behaviour and brain functions. To this end, even the transient alteration of
sleeping patterns, including severe sleep deprivation, can impair one's cognitive
performance and judgment, even as prolonged aberrations have been associated with
the development of disease. The existing global sleep trends indicate a decrement in
average sleep durations. Owing to such trends and the various implications of sleep on
human well-being and health, enhanced characterisation of the sleep attributes indicates
a public health priority.
Further, the advancement and use of multi-modal sensors with technologies to monitor
physical activity, sleep, and circadian rhythms have increased dramatically in recent
years. For the first time, accurate sleep monitoring on a large scale is now possible.
However, there is a need to overcome several significant challenges to realise the full
potential of these technologies for individuals, medicine, and research. In this chapter,
a review of the present levels of the sleep-monitoring technologies in patients with
cognitive impairments, in addition to assessing the difficulties and potentials lying
ahead, from data gathering through the ultimate execution of findings within the
consumer and clinical contexts.. Further, the chapter will review the advantages and
disadvantages of the extant and novel sensing technologies, focusing on new datadriven technologies that include Artificial Intelligence.
Early prediction in AI-enabled IoT environment
Page: 85-99 (15)
Author: Ambika N.*
DOI: 10.2174/9789815196054123050008
PDF Price: $15
Abstract
IoT is intelligent sensors and actuators which assemble to form an IoT device. The algorithms employed make the system make up a wise decision. These systems can use artificial Intelligence algorithms to make intelligent decisions. The previous work employs devices that compute normal from abnormal heart rates. These devices are intelligent machines that are carried with the individual. They are also used to calculate the ECG of the personnel. This information understands the behavior of the personnel. The knowledge is sensed and passed to the devices using the Bluetooth technique. This data segment into healthy or unwell being sections. The processing amalgamates transformation, conversion w.r.t format, and section labeling. The iforest approach excludes the outliers from the data set. The suggestion improves the previous work by predicting the abnormality before in hand by 17.5%. Many lives can be saved, and will help improve their lives by adopting this method.
AI and Blockchain-based Solution for Implementing Security for Oral Healthcare 4.0 Big data
Page: 100-134 (35)
Author: Sreekantha Desai Karanam*, Niriksha Shetty and Rahul Bhandari
DOI: 10.2174/9789815196054123050009
PDF Price: $15
Abstract
The patient's medical data is very valuable and sensitive. Hackers are always
trying to steal or tamper with this patient's data. Cyber-criminals can misuse or sell this
patient's data. Protecting medical data, and ensuring security and privacy of data is a
statutory and ethical mandate for healthcare services providers. Implementation of
blockchain technology enhances the security, confidentiality, and traceability of patient
data. Secure sharing of patient data among all stakeholders of healthcare ecosystems
ensures quality and high speed in services.
Objective: This paper's objective is to review data security applications in the
healthcare domain using blockchain technology and present the highlights from
selected survey papers. The second objective is to implement data security using a
web-based prototype based on blockchain technology.
Method: This blockchain technology enables secure online sharing of data using open,
distributed, denationalized, and immutable ledgers. Blockchain-based online
transactions are taking place between sender and receiver directly. Blockchain-based
transactions eliminate the need for third-party intermediate agents. Data. Authors
applied blockchain technology for the implementation of oral health big data using a
web portal.
Results: The taxonomy of concepts applied in blockchain and EHR, Smart Contracts,
and Healthcare 4.0 systems in healthcare from the literature are mapped. To identify
research gaps, a comparative study of curated survey papers is conducted with specific
variables. The results of web portal implementation are discussed.
Conclusion: At the out, the authors tried to provide a holistic view of blockchain
applications and presented insight into systems design by discussing algorithms,
techniques and methods applied. Authors conclude that intelligent systems integrated blockchain systems are going to play a vital role in the healthcare industry and enhance
the quality and efficiency of services.
An Artificial Intelligence-based Method for Detecting False news in Health Sector During a Pandemic
Page: 135-150 (16)
Author: B. Sahana*, B. Sadhana, Mamatha Mohan and Sindhu Rajendran
DOI: 10.2174/9789815196054123050010
PDF Price: $15
Abstract
Recently, fake news has become a serious problem in our society majorly
due to the cheap and easy availability of social media at every corner of the world. The
widespread dissemination of false news has the potential to have a variety of harmful
consequences for people and society. Hence, many researchers are finding different
ways to detect fake news in a given news corpus. So here we came up with the idea of
fake news detection using machine learning that detects fake news over the real news.
During pandemic, fake news detection played an important role. Detection and
identification of fake news in the social media, or any related news channels has played
a major responsible sector to avoid unnecessary panic situation in mankind.
This paper is aimed at developing a Machine Learning model for deception detection
using Natural Language processing techniques and machine learning algorithms. It
detects fake news that comes from non-reputable sources which mislead people and
distracts them with various fraud messages and unnecessary texts, by building a model
using count vectorise, TF-IDF and logistic regression algorithm. Using this algorithm,
the proposed technique identifies and rectifies real and fake news and this is an
important sector during the pandemic situation.
However, there is difficulty in choosing the right metric for the evaluation of the
model. Classification accuracy is one of the most used metrics to detect the
performance of the model, in this paper we consider the parameters such as F1 score,
confusion matrix, precision and recall. Abstract environment.
Intelligent Framework for Smart Health Application using Image Analysis and Knowledge Relegation Approach
Page: 151-165 (15)
Author: Akhila Thejaswi R.*, Bellipady Shamantha Rai and Permanki Guthu Rithesh Pakkala
DOI: 10.2174/9789815196054123050011
PDF Price: $15
Abstract
The future direction of modern medicine is toward “smart healthcare,”
which incorporates a new generation of information technology to meet patient needs
individually while increasing the effectiveness of medical care. This greatly improves
the patient experience with medical and health services. Nowadays, due to people's
lifestyles, diabetic retinopathy is one of the most serious health issues they confront. A
deviation from the norm in which long-term diabetes affects the human retina is called
diabetic retinopathy (DR). Diabetes is a chronic condition related to an expanding
measure of glucose levels. As the degree of glucose builds, a few adjustments happen
in the veins of the retina. Patients' vision may begin to deteriorate as their diabetes
progresses, resulting in diabetic retinopathy. It is exceptionally far-reaching among
moderately aged and older individuals. Thus there is a need to detect diabetic
retinopathy at an early stage automatically. This study aims to build an intelligent
framework that uses fundus images of the eye (retina) and performs image analysis to
extract the features. Images are trained by the knowledge relegation approach, and the
severity of the DR is classified using K-nearest neighbors. The proposed model
achieved a test accuracy of 99%, 61%, 100%, 94%, and 88% for each of the five
classes of diabetic retinopathy: proliferative diabetic retinopathy, no diabetic
retinopathy, mild diabetic retinopathy, moderate diabetic retinopathy, and severe
diabetic retinopathy.
Brain Stroke Prediction Using Deep Learning
Page: 166-178 (13)
Author: N.V. Maha Lakshmi, Sri Silpa Padmanabhuni*, B. Hanumantha Rao, T. Krupa Nandini, T. Sai Teja and U. Vamsidhar Reddy
DOI: 10.2174/9789815196054123050012
PDF Price: $15
Abstract
A brain stroke is a disruption of blood circulation to the cerebrum. As per
recent analysis, adult death and disability are primarily brought over by brain stroke.
The World Health Organization (WHO), reports that the primary cause of death and
property damage worldwide is brain stroke. Early detection of the signs and symptoms
of a stroke can help to reduce risk factor of death by up to 50%. A stroke is more likely
to occur in adults over the age of 55. An increasing number of people are experiencing
this crippling and frequently fatal form of stroke, which results in cerebral hemorrhage.
Various machine learning (ML) models were developed to predict the possibility that a
brain stroke would occur. To predict the brain stroke, the proposed system used the
CNN algorithm. The existing approaches are k-NN, Support Vector Machine (SVM),
Genetic Algorithm (GA), Naïve Bayes classifier, J48 algorithm, Logistic Regression
(LR) and Random Forest (RF). This requires more time to train the model and it is
difficult to debug. And these are not suitable for large datasets. The proposed system
makes predictions using CNN algorithm, a deep learning technique. It includes a
multilayer perceptron for the prediction task and an autoencoder for eliminating and
capturing non-linear correlations between parameters. The proposed system is
contrasted with existing system and it shows an enhancement in the capability to
anticipate the stroke. The proposed system achieved an accuracy of 89%.
Secure Electronic Health Records Sharing System using IoT and Blockchain
Page: 179-190 (12)
Author: Harisha A.*, Shiji Abraham, Srinivas P. M., Praveen Honavar, Kavya, B. M. Nischitha and Manish Poojary
DOI: 10.2174/9789815196054123050013
PDF Price: $15
Abstract
The Electronic Health Record (EHR) is used for maintaining patients’
medical records in the hospital. The EHR contains the details of clinical related data of
the patients under a particular provider. The EHR contains information on a patient's
demographics, medications, previous medical history, laboratory results, and reports
like X-rays. The EHR system is introduced in order to share the details with other
health providers such as laboratories, pharmacies, emergency facilities, and clinics so
that they will have all the medical history of the patient’s health conditions. These can
be accessed from anywhere via any smart device. A single record for one patient across
all departments. Sharing EHR with different health care providers is a major challenge
since this health record is stored on centralized servers patients cannot share this
information when required. To overcome this issue we have come up with an approach
of using IPFS (Inter Planetary File Systems) to store this EHR in a decentralized
manner and an RSA algorithm to encrypt the Health Record. By using the combination
of blockchain and cryptography a secure platform can be developed for providing the
patient with full control over their health record and also maintaining data integrity.
Geofencing For Elderly
Page: 191-208 (18)
Author: Jidynasa Patil* and Anita Chaware
DOI: 10.2174/9789815196054123050014
PDF Price: $15
Abstract
This century has witness a substantial increase in elderly population. Health
issues like depression and dementia are more prominent in these elderly populations
which demand Assisted Living environment. The engagement of technology is seen as
a solution for the Assisted Living environment. With the help of technology, Ambient
Assisted Living (AAL) has become a field of research. As AAL strives to seamlessly
connect information technology with people's daily lives, the buzz word called Internet
of Things (IoT) exhibits significant promise for developing technical solutions in this
field. Geo-fencing is one such location sensing tool that uses IOT and GPS for defining
geographical boundaries and is used for putting e-fences to the needy people in their
ambiances. This article aims to provide a safe tracker environment that allows the
elderly people to continue with their daily activities. In this article, application built for
elderly people is explained. Through this application, the person gets the direction to
reach back home or the alert message is sent to the family member or the caretaker.
The alert message is sent while the person is out of the fencing area to himself or to the
care takers and the location of the person can be tracked. This app can also be modified
for different users like person with disability, game like pub-G players etc. for the
situations where the device sense that the person is in a danger zone or out of the geo-fencing.
I am the Eye - Assistive Eye
Page: 209-224 (16)
Author: Muadh Bin Mohammed Ali*, Mohammed Shayiz K. P., Habeeb ur Rehman, Mohammed Nouman and Mohammed Thajuddin Sanad
DOI: 10.2174/9789815196054123050015
PDF Price: $15
Abstract
The vision of this device is to design and construct the blind-friendly
embedded device. The blind and visually handicapped have difficulty utilizing mobile
phones because social media and online banking programs on smartphones are difficult
for them to utilize. For quick bank transactions, ATMs are used. If blind individuals
use the ATM and it isn't designed with visually impaired persons in mind, there will be
privacy concerns. Using mobile phones with the assistance of others may jeopardize
their security and privacy. Touch screens were not designed with visually impaired
persons in mind. They are uneasy using cell phones in public due to current
technologies. When visually impaired persons walk, they use a stick, which can be
replaced as well. By gaining access to all capabilities of smart phones, the developed
system would assist visually impaired persons in making their lives much easier.
Stage of Retinopathy of Prematurity Using CNN and Object Segmentation Technique
Page: 225-239 (15)
Author: Jothimani K.*
DOI: 10.2174/9789815196054123050016
PDF Price: $15
Abstract
Premature adolescents with Retinopathy of Prematurity (ROP), a
fibrovascular proliferative condition, have difficulties with the maturing peripheral
retinal vasculature. Early identification of ROP is achievable in stages 1 and 2,
distinguished by a demarcation line and ridge that divides the peripheral retina from the
vascularized retina. Because newborn retinal images have poor contrast, it is difficult to
distinguish demarcation lines or ridges. This study used segmentation and
convolutional neural networks to detect ridges, which are crucial landmarks in the
diagnosis of ROP. Our contribution is implementing Mask R-CNN for identifying
boundary line/ridge recognition, which enables doctors to identify ROP stage 2 more
accurately. To combat poor image quality, the suggested approach uses a pre-processing stage of image augmentation. In this study, the utility of the Convolutional
Neural Network was examined to localize ridges in labeled neonatal photos. The
KIDROP study and a dataset comprising 220 photos of 45 infants were used. Using the
segmentation of the ridge region as the ground truth, 175 retinal images were used to
train the system. The system's detection accuracy was 0.94, with 45 images under test,
proving that data augmentation detection in conjunction with image normalizing pre-processing allows accurate identification of the ROP in its early stages.
An Overview of Recent Medical Applications of Soft Robotics
Page: 240-247 (8)
Author: Manoj, P. C. Shrihari, Gouda Shankar S., M. Pramod Rao and Rathishchandra R. Gatti
DOI: 10.2174/9789815196054123050017
PDF Price: $15
Abstract
Soft robotics is one of the trending subdomains of robotics. It involves the
application of compliant materials for building robotic mechanisms and controlling
them using robot programming. This chapter discusses some of the recent applications
of soft robotics in the medical field and their future scope. Minimal Invasive Surgeries
(MIS) and Natural Orifice Transluminal Endoscopic Surgeries (NOTES) are the two
commonly used surgeries, and the most familiar form of these two surgeries is
endoscopy. In this chapter, we will discuss how soft robotics can be applied in both
MIS and NOTES. This chapter will review the soft robotics applications in the medical
field. This chapter will also discusses soft robotics's challenges and future directions in
the healthcare industry.
Applications of AI-enabled Robotics in Healthcare
Page: 248-261 (14)
Author: Blaren D’Silva and Rathishchandra R. Gatti*
DOI: 10.2174/9789815196054123050018
PDF Price: $15
Abstract
Robotics began roughly 30 years ago in medical applications, but it is still
relatively young for biological applications. Because of the precision, accuracy and
reproducibility of robotic technology, robotic interventions in medical fields, such as
robotic surgery, can enable doctors to work inside the human body, which is either
non-invasive or minimally invasive, with improved surgical results. The importance of
medical robots in the medical sector is intended to deliver good outcomes to assist
people in doing complex tasks that need a significant amount of time, accuracy,
concentration, and other routines that cannot be accomplished solely through human
capability. Due to advancements in AI and IoT and their convergence to AIoT, the
potential of medical robots has tremendously increased in the healthcare industry. The
chapter outlines the various applications of robotics in the healthcare sector, including
surgical, rehabilitation, telemedicine, and diagnostic. The advantages of robotics in
Healthcare are highlighted, along with the discussion on the current and future
challenges in their deployment and adoption. The role of AIoT in enhancing these
healthcare robots' cognitive and other capabilities is also discussed. Finally, the future
of robotics in Healthcare is explored, including emerging trends and technologies, their
impact on the healthcare industry, and the potential for innovation and growth.
An Overview of Current and Future Applications of Robotics In Surgical Operations
Page: 262-272 (11)
Author: Veerishetty Arun Kumar, Nishan Rai, A. R. Badrinath, Abhishek Kamath and Rathishchandra R. Gatti*
DOI: 10.2174/9789815196054123050019
PDF Price: $15
Abstract
Technology has changed almost all aspects of our life. Similarly, in the
medical field, the new technology is Robotic surgery. Robotic surgery involves
employing robots in the process of surgery. The employed robot, known generally as
the surgical robot, is self-regulating, partially or entirely computer-controlled, and can
be programmed as required for the surgery. As different surgical robots are employed
for different types of surgery, robotic surgery improves patient care and ensures better
treatment than regular surgery. The purpose of this article is to provide an outline of the
main ideas of robotic applications used in surgery. This article aims to provide an
overview of robotics's current and future applications in surgical operations and the
advantages and disadvantages of surgical robots.
Healthcare Applications Centered on AIoT
Page: 273-289 (17)
Author: Sapna R.* and Preethi
DOI: 10.2174/9789815196054123050020
PDF Price: $15
Abstract
The Internet of Things (IoT) is a quickly expanding environment which
combines software, hardware, physical components, as well as computing tools for data
collection, sharing, or rather interaction. The IoT enables a unified platform for humans
to interact with a wide range of physical and virtual objects, like personalised
healthcare domains. Due to the explosive growth and advancement of the internet,
traditional patient care strategies have enhanced with the replacing e-medical records
mechanisms. The use of IoT technology provides medical modern healthcare
equipment device setting for both physicians and clients. IoT devices and Artificial
Intelligence are beneficial in many implementations, starting with remote weather
monitoring to mechanical mechanisation. Furthermore, medical care applications are
showing a strong interest in IoT devices due to cost savings, easiness of using it, and an
increase in service quality. The most recent services for IoT-based healthcare, which
have been investigated and are still facing challenges in the clinical setting, are
required for intellectual, creative solutions. An exploration of prospects for artificial
intelligence and the internet of things in the medical sector is provided in this chapter.
Subject Index
Page: 290-295 (6)
Author: Shreyas Suresh Rao, Steven Lawrence Fernandes, Chandra Singh, Rathishchandra R. Gatti, Harisha A. and Rohanchandra R. Gatty
DOI: 10.2174/9789815196054123050021
PDF Price: $15
Introduction
AIoT (Artificial Intelligence of Things) and Big Data Analytics are catalyzing a healthcare revolution. This book is an accessible volume that summarizes the information available. In this book, researchers explore how AIoT and Big Data can seamlessly integrate into healthcare, enhancing medical services and devices while adhering to established protocols. The book demonstrates the crucial role of these technologies during healthcare crises like the COVID-19 pandemic. It presents novel solutions and computational techniques powered by AIoT, Machine Learning, and Deep Learning, providing a new frontier in healthcare problem-solving. Key Features: Real-Life Illustrations: Real-world examples showcase AIoT and Big Data in action, highlighting their impact in healthcare. Comprehensive Exploration: The book offers a thorough examination of AIoT, Big Data, and their harmonious synergy within the healthcare landscape. Visual Aids: Complex concepts become approachable through diagrams, flowcharts, and infographics, making technical processes and system designs more digestible. Ethical Insights: Delving into the ethical dimensions of AIoT and Big Data, it addresses concerns like data bias, patient consent, and transparency in healthcare. Forward-Looking Discourse: The book engages with emerging trends, potential innovations, and the future direction of AIoT and Big Data, making it a compass for healthcare transformation. Researchers, whether from academia, industry, or research and development organizations, interested in AIoT, Big Data, artificial intelligence, and healthcare optimization, will find this book informative. It also serves as an update for tech enthusiasts who want to explore the future of healthcare powered by AI and data.