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Using the research article selected for DQ 1, identify three key questions you will ask and answer when reading the research study and why these questions are important.

Healthcare Research
Student Name
Institutional Affiliation
Healthcare research.
The authors provide an overview of the use of blockchain and artificial intelligence
in personalized medicine in this study. They show how these technologies could
help provide more personalized and efficient care. They also identify some
challenges that must be addressed before these technologies can be fully effective.
In conclusion, they show that blockchain and artificial intelligence have tremendous
potential in the field of personalized medicine, but that more research is required
to fully realize this potential.
Sampling strategy
The first step in the selection process was to identify the study population. The
population, in this case, was those patients with chronic condition therapy. After
identifying the population, a sampling frame was made. The sampling frame is a list
of all the people in the population that can be used to choose the sample (Manna
& Mete, 2021). For this study, the sampling frame was made by looking at medical
records from a large health care provider.
A random group of patients was chosen from the sampling frame. Once the
researchers had chosen the sample, they got in touch with the patients and asked
them to take part in the study. The last step in choosing people was to make sure
that the sample was a good representation of the whole population (Flanigan et al.,
2021). The researchers did this by looking at the similarities and differences
between the sample and the whole population. This gave the researchers a chance
to see if there was any bias in the sample and make changes as needed.
Sampling method used
Convenience sampling was applied in this study. It is effective in small population
studies. This study’s population is small because a very small percentage of the
general population are both having chronic illnesses and seeking medical
intervention (Hu et al., 2021). In this study, the researcher looked at blockchain and
AI in personalized medicine and how they can improve patient care. The
convenience sample was effective since it produced a representative sample. This
ensures that the study’s results were accurate and applicable to a larger group. The
method also saves time and money.
Flanigan, B., Gölz, P., Gupta, A., Hennig, B., & Procaccia, A. D. (2021). Fair algorithms
for selecting citizens’ assemblies. Nature, 596(7873), 548-552. Fair algorithms for
selecting citizens’ assemblies. Nature, 596(7873), 548-552. https://rdcu.be/cSxGN
Hu, H., Jian, W., Fu, H., Zhang, H., Pan, J., & Yip, W. (2021). Health service
underutilization and its associated factors for chronic diseases patients in povertystricken areas in China: a multilevel analysis. BMC Health Services Research, 21(1), 114. https://lopes.idm.oclc.org/loginurl=https://search.ebscohost.com/login.aspxdirec
Manna, R., & Mete, J. (2021). Population and sample. International Journal of
Research and Analysis in Humanities, 1(1), 30-30. https://www.iarj.in/index.php/ijrah
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Conferences >2022 6th International Confer…
Healthcare 5.0: A Study on Improving
Personalized Care
Publisher: IEEE
Cite This
Rohit Verma; Samrat Patel; Saviour Viyadis Minj; Aruna Bhat
All Authors
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Document Sections
Blockchain, Artificial Intelligence, And Healthcare
Related Works
Result And Future Works
Medicine has been chiefly a reactionary profession throughout history. Even
today, one has to wait until diseases have manifested before attempting to
treat or cure them. And because the genetic and environmental components
that contribute to major diseases like Cancer, Alzheimer’s and cardiovascular
diseases are not fully understandable, our efforts to treat them are often
haphazard, unpredictable, and inefficient. As a result, the drugs and therapies
developed are evaluated on large groups of people and given based on
statical averages. And because of the genetic variances, they function for
some patients but not for others. Every prescription medicine now on the
market only works for half of the people who take it. Based on each patient’s
unique genetic composition and environment circumstances, personalized
medicine is a critical milestone to be attained to address this issue. Artificial
intelligence and blockchain have recently been innovations in the healthcare
industry. Furthermore, blockchain can control patient data privacy among
participants. It uses asymmetric cryptography to secure transactions between
users without the need for any intermediary or a trusted third party, thanks to
smart contracts. On the other hand, Artificial intelligence provides intellect and
decision-making capabilities for robots similar to humans. This study also
identifies and analyses open research problems associated with blockchain
and artificial intelligence in personalized medicine.
Published in: 2022 6th International Conference on Intelligent Computing and
Control Systems (ICICCS)
Date of Conference: 25-27 May 2022
Date Added to IEEE Xplore: 08 June 2022
ISBN Information:
ISSN Information:
DOI: 10.1109/ICICCS53718.2022.9788411
Publisher: IEEE
Conference Location: Madurai, India
Medicine’s advancement brings us closer to more accurate, dependable, and
robust health care tailored to each unique patient. Our growing understanding
of genetics and genomics, as well how they influence health, disease, and drug
responses in individuals, allows doctors to provide improved preventive
medicine, more precise and reliable diagnosis and treatment, safe medication
prescriptions, and more effective care for the many diseases and disorders that
wreak havoc on our health. Precision medicine, often known as personalized
medicine or genomic medicine, is a promising concept that aims to tailor
medical services to each person’s specific genetic makeup and is starting to
transcend traditional medicine’s limits. Predicting disease susceptibility,
improving disease detection, preventing disease progression, customizing
disease prevention strategies, medicating more effective methods, minimizing
prescribing drugs with observable side effects, reducing the amount of time,
expenditure, and percentage of failure of medicinal clinical studies, and
abolishing trail-and-error inadequacies that drive up healthcare costs are all
possible with personalized medicine[1].
The three most significant developments are the digital twin, blockchain, and
AI to achieve this goal. Blockchain is nowadays the most discussed technology,
and it is gaining vast space in various industries[2]. Decentralized artificial
intelligence (A.I.) combines intelligent machines and blockchain networks[3].
Without using Trusted Third Party APIs or intermediaries, the distributed and
decentralized A.I. provides analysis and course of action on reliable,
authenticated, and shared secured data preserved and processed on the
blockchain[3][4]. AI can work with massive amounts of data, and blockchain
provides a stable network for safeguarding this data. The function of smarts
contracts is its capacity to design. The function of smart contracts is its
capacity to design the ledger to handle transactions between involved parties,
such as decision-making, producing and analyzing information[5].
Autonomous systems and machines built on smart contracts can train and
respond to changes over time, resulting in reliable and precise decision
outcomes confirmed and certified by blockchain miners. Any participants
cannot refute such decisions that can be traced, followed, and validated. A.I.
approaches based on blockchain technology can be used to accomplish
decentralized learning, allowing for the safe and stable transfer of information
and end result across a significant number of autonomous entities who can
participate, cooperate, and make future decisions [6][7]. Digital twins are
computer-based digital representations of human genetics that incorporate
data from individuals and communities. Researchers exploring diseases, new
medications, and medical technologies benefit significantly from digital twins.
In the future, digital twins could aid physicians in improving the effectiveness
of patient-specific treatment programs and developing individualized therapy
routines. Digital twins will enable the healthcare sector to bring life-saving
breakthroughs to market faster, at lower costs, and with higher patient safety
in the short term. For example, “digital twins” of organs like the heart or tissue
are feasible for individual patients. Then simulations may be used to see how
different people respond to various treatments. However, digital twin
technology can reflect an individual’s genes, physiological traits, and lifestyle
to fully tailor therapy. Doctors can use a digital twin of a human body to detect
pathology before symptoms appear, experiment with medicines, and prepare
for surgery. However, large-scale application is the key to turning digital
twins’ potential into genuine impact: making the technology broadly
accessible in daily routines, reinventing essential clinical processes using
computerized simulations, and enhancing healthcare services.
There are currently no comprehensive reviews of research on the function of
digital twins in customized medicine in the literature. According to existing
literature, researchers explored digital twins, blockchain, and artificial
intelligence (A.I.) in independence and have found their applicability in
numerous vertical sectors and other businesses. A few studies looked into the
intersection of digital twins, artificial intelligence, and blockchain, as well as
the consequences for how we live, work, engage, and transact [3].
Everyone is now required to visit the hospital for testing and diagnosis, which
takes time and money. As a result, patients with critical ailments will not see
the nearest hospital. To address this issue, the healthcare sector has
implemented Remote Patient Monitoring. Patients’ Wearable Devices/IoTs
record real-time health data such as ECG, pulse rate, temperature, and blood
pressure. This data can assist doctors in making diagnoses, and with the
support of decentralized AI, they can provide health-related recommendations
to their patients. Patients can be diagnosed remotely by clinicians using this
method [2], which enhances the quality of treatment.
Acquiring real-time patient records remotely via electronic devices attached to
the patients that transmit vital sign data or information, most often
accomplished by cellular or Bluetooth/Local network, is RPM’s most
demanding and prioritized application area. The current technology collects
real-time data from Wearable Devices/IoTs and stores it in a blockchain
network in a ledger, which keeps track of the changes in the patient’s private
The rest of the paper is presented as follows. Section II dives into the history
of AI and blockchain technology and how blockchain might aid in the
transformation of existing AI methodologies. Section III presents the work
done in the related field. Section IV presents results and future work in
personalized medicine using blockchain applications, A.I. Section V concludes
the paper, and Section VI shares the references.
Blockchain, Artificial Intelligence, And
A. Blockchain
One of the most popular medical usages currently is to keep our valuable
medical data which contain vital information about the patients, as it is very
personal information, safe and secure from cyber attacks, which lead to
unethical use of the valuable data. As in the healthcare sector, security is a
major concern. Healthcare corporations will have
spent 6trilliononsecurityvulnerabilitiesby2020.Themedicalindustryisanti
cipatedtospend65 billion on security during 2017 and 2021 [8]. The attackers
stole a health and genetic screening report along with the credit card
Fig. 1.
Individuals affected by healthcare data breaches [9]
Show All
Blockchains’ capacity to keep an irreversible, distributed, and accessible log of
all patient records makes them suitable for security systems. Additionally,
while blockchain is visible, it is also confidential, masking any patient’s
identity using sophisticated and secure protocols capable of safeguarding the
specificity of medical information. All parties, i.e., medical professionals,
patients, and those in the medical field, can all access the very same
information swiftly and safely thanks to the technology’s decentralized
structure [10]. By deflating the present expenditure bubble, preserving patient
information, and providing a better experience, blockchain in medicine could
help alleviate the frustration. In terms of removing the requirement for a
centralized authority to manage and verify activities and operations among
various parties, blockchain has the ability to be incredibly cost-effective. All
miners, which keep a copy of the whole ledger comprising chained blocks of all
activities, cryptographically identify and verify all transactions upon the
ledger. This results in secure, synchronized, and shared timestamps records
that can’t be tampered with [11].
B. Artifical Intelligence
Artificial intelligence is another popular field that allows machines to learn,
deduce, and respond based on the information they collect by using massive
amounts of data combined with fast, iterative processing and intelligent
algorithms; Al algorithms are able to learn automatically from data patterns
and features.
According to a new study released, the the artificial intelligence sector will be
worth $13 trillion around 2030. The growth of A. I have been aided by
wearable sensors, the Internet of things, social networks, and internet
applications [12]. Such information can be used to perform a wide variety of
data analyses using artificial intelligence techniques [13]. To date, most
artificial intelligence (AI), machine, and deep learning processes depend on a
centralized learning algorithm. A set of servers uses training and validation
datasets to deliver an effective model. Numerous companies, such as Bing,
Amazon, Fb, and Walmart, manage enormous amounts of data in order to
make smart judgment[4].
TABLE I. Combining Blockchain and A.I.
The centralized structure of A.I., on the other hand, can result in information
being tampered with since central data storage can be compromised and
modified [3]. Moreover, the origination of the information and the validity of
the source with which it was collected cannot be ensured [5]. As a result, the
outcomes of A.I. selection may be wildly inaccurate, harmful, and unsafe.
C. Healthcare
Healthcare care is defined as the preservation or improvement of one’s health
through the avoidance, identification, therapy, rehabilitation, or treatment of
illnesses, sickness, accidents, as well as other health-related conditions in
humans. Care is provided by health professionals and those in related health
sectors. Medical, dental, pharmacology, osteology, nursing, ophthalmology,
audiology, counselling, rehabilitation services, physiotherapy, sports training,
and other health sciences all fall under the category of “health care.” Public
health and primary, secondary and tertiary care are all included in
healthcare [14].
Medical practitioners who serve as the first point of access for all patients
within the medical system are referred to as primary care providers. A primary
care doctor, such as with a general medical practitioner or family doctor, is
usually a trained medical expert. Two more options are a licensed medical
practitioner, such as a physical therapist, or a non-physician personal
healthcare professional, such as a physician aid or certified nurse midwife.
The patient may first visit with another health care provider, such as a chemist
or pharmacist, depending on the region of the health system institution. Based
on the patient’s ailment severity, they may be recommended for secondary and
tertiary care.
Related Works
Today, distributed ledger technology has advanced to the point that it can be
applied in various industries, including banks, economics, and medicine.
We’ve primarily focused on how blockchain technology could enhance the
existing health service in the case of remote supervision in treatment. This
section contains the related research works done in the past, and we will give a
brief overview of their state of the artwork. As described by researcher Zheng
et al. in 2019, a network with the flexibility of sharing data by synchronizing it
with the Internet – of – things and Smart Wearable IoTs helps secure, make
counterfeit-proof, and a sustainable medical system [15]. A model with lower
parameters can perform well when used with knowledge distillation and
hybrid distillation (knowledge + attention) for medical image classification.
This model was developed by Aakash Garg et al. [16] using a CNN architecture
that utilizes knowledge distilling and hybrid techniques.
Another author et al. [17] presented a survey integration of a permission
blockchain with ML for their review. A blockchain that can be used to identify
disease in advance and send a relevant report accordingly would help take
preventive measures accordingly. The infection prevention and control system
was developed using the ML algorithm by the author et al. [18].
Yue et al. [18] have a single cloud system with access restrictions for approved
users and patients’ healthcare data. This gives patients clinical data access,
monitoring, and management. A s mart contract-based private ledger
approach provided by Griggs et al. [19] improves the security in healthcare in
the case of remote monitoring. This allows doctors to keep track of their
patient’s health state utilizing a real-time remote monitoring system from afar,
as well as a secure and safe way of accessing records. In [20], the proposed
method for early detection of skin cancer classifiers uses Neural Architecture
Search and Model Quantization technique to outperform existing models.
Chen et al. [21] gave a cloud-based solution that provided more secure and
confidential storage for easy data exchange. A public blockchain with
encrypted health data gave patients the access to share, monitor, and
contribute their healthcare data accordingly by Ivan et al. [22]. In [23], the
author has discussed a study of works that uses X-ray to determine whether a
person is Covid Positive or Negative. He found that models used imbalanced
open-source data in which covid positive samples were considerably lower
than normal samples. This shows a lack of data actually to generalize models
for the task. The paper [24] has proposed a model that uses GAN to generate
synthetic chest CT images of both positive and negative Covid Patients. Using
the Deep Convolutional GAN for generating images, the model could predict
from the generated image the covid positive images .
Wang et al. [25] gave a digital ledger architecture based on parallel execution
and an A.I. powered healthcare system that could analyze the condition of the
healthcare data and help diagnose the patients. Then related to the diagnosis,
the treatment process can be given to the patients. This is achieved by the
parallel execution and computational traits for the proper diagnosis and
treatment decision-making. This framework has been tested for the evaluation
of accuracies of diagnosis and effectiveness. A framework for prediction and
medication of Breast Cancer using the smart contract that legitimately
improves the safety of the patients’ data in hospital and patients in the
comfort of the houses by using remote monitoring as given by Shubaar et
al. [26].
Result And Future Works
Nowadays, using blockchain has opened a significant number of opportunities
when it comes to other applications apart from bitcoin. Through blockchain,
we can remove the central authority and eliminate the system’s risk of failure.
With blockchain, it will increase the accuracy and efficiency of machine
learning models and so their usability, as the data stored into the server will
not have any missing or error values, duplicates, and noise associated with it,
which are one of the primary requirements for higher accuracy of ML models.
In the future, the use of Machine Learning would be beneficial to both patients
and doctors since it will directly correlate with the life of a person, and we will
be able to predict diseases from trend data or symptoms of a patient at very
early stages, as we are able to do for brain tumors, etc. Also, the probability of
the treatment prescribed by the system being actually helpful to the patient
will be very high as the medicine/treatment prescribed to them will be
personalized to their lifestyle and genetics, which can be achieved with their
vital data collected from various sources like WIoTs, laboratories, etc., and
extracting meaningful patterns from them using AI. Practical implementation
of this model is the future scope of this. Below are the challenges in a
blockchain that need to be addressed for future scope.
Security and Privacy
Limitations in Smart Contracts.
Limitations in Deterministic Execution.
Novel Consensus Mechanisms for AI.
A New Approach to Computing in the Fog.
There are no standards.
There is no interoperability
No Regulation
Also, accuracy performance measures should not solely measure the quantity
of the overall model performance. The model is highly susceptible to factors
like network bandwidth, quality and quantity of vital information of patients
in the network, net cost of the hardware, and maintenance cost.
This article reviewed and surveyed the current state of the art application of
blockchain, digital twins, and artificial intelligence features for customized
medicine. We discussed how blockchain technology might improve and solve
crucial difficulties in customized treatment by providing a brief of blockchain
and decentralized storage. We also discussed and compared conventional
blockchain implementations in decentralized A.I, operations, blockchain kinds
and infrastructure, and consensus methods. A study of digital twin
applications is conducted in terms of tailored therapy. Various aspects of
digital twins, artificial intelligence, and distributed ledgers are also discussed.
Our literature review indicates that using digital twins and blockchain for A.I
enabled application areas are still in their early stages, with many research
gaps to be addressed and overcome in areas such as confidentiality, smart
contract reliability, trustable oracles, expandability, consensus methods,
standardization, interconnectivity, quantum information stability, and
democratic accountability.
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