Research Report: Importance of Machine Learning in Medicine Analysis

This comprehensive research report investigates the critical role of machine learning in the medical field, exploring its applications, benefits, and ethical implications. The study delves into the concept of machine learning, providing examples like the Brain Age Project and its contributions to medical advancements. It examines the ethical challenges associated with AI in healthcare, offering recommendations to mitigate potential issues. The research employs both primary and secondary data collection methods, including expert interviews, to analyze the impact of machine learning on healthcare professionals and patients. The report also covers limitations and caveats of the study, providing a balanced perspective on the topic. The findings highlight the transformative potential of machine learning in revolutionizing medical diagnosis, treatment, and research, emphasizing the need for continuous innovation and ethical considerations within the industry. This report is a valuable resource for anyone interested in understanding the current and future impact of machine learning on medicine.

Business Research Method 1
Business Research Method Proposal

Business Research Method 2
Executive Summary
The research study is taken into consideration for the purpose of identifying the importance of
machine learning in medicine. The use of technology has developed the medical industry in an
extended edge. In relation to this study, the role of ethical aspect in context of machine learning
in medicine is also explored in depth manner. This study is carried out with sample size of 18
respondents as the experts in medical industry. Additionally, the primary and secondary data
have been used to gather the data from targeted population. The problem for this research is
defined as the utility and importance of machine learning in the medicine. On the other hand, the
research study is limited in sampling size, data, time and financial resources. Over the data
analysis, it can be concluded that the machine learning is important in the medicine to innovate
the new ways of resolving the medical disease in most feasible manner.

Business Research Method 3
Table of Contents
Executive summary.........................................................................................................................2
Introduction......................................................................................................................................4
Research objectives.........................................................................................................................5
Research questions...........................................................................................................................5
Literature Review............................................................................................................................7
To investigate the importance of machine learning in medicine.................................................7
To assess the ethical acceptance of machine learning in medicine:..........................................10
Research Methodology Design......................................................................................................12
Sampling........................................................................................................................................15
Limitations and Caveats of study..................................................................................................17
Data analysis and interpretation.....................................................................................................18
Conclusion.....................................................................................................................................29
Recommendations..........................................................................................................................30
Reference.......................................................................................................................................31
Appendix........................................................................................................................................35
Questionnaire.............................................................................................................................35

Business Research Method 4
Introduction
The medical industry is one of the growing industries in the global market. In relation to this, the
advancement of technology has also developed the medical industry in a new era of competitive
environment. In the recent years, the application of machines in diagnosis of disease has also
increased. The main aim of this study is to explore the role and importance of machine learning
in medicine with respect to its development and advancement in technology. Along with this,
this investigation is also carried out with respect to the assessment of ethical accept of machine
learning in the medicine. The literature is described with the inclusion of proposition and
hypotheses about the utility of machines in medical industry.
The main reason for carrying out this research is to identify the practical implications of
machines in the medical industry and how the machines are supporting the doctors and
researchers. This study is focused on determining the applicability of machine learning to
enhance the relevancy of technological system in advancement of medical industry. The chosen
topic is relatively important for the investigation as it has came into the light with the emergence
of hi- tech machines in medical problem diagnosing. At the same time, it is also crucial to study
as the technology has given the automated machines that are useful to evolve the new medicine
and automatic diagnosis for medical treatment. With relation to this, the investigation is also
deriving implications on the medical students to explore the knowledge about their study. Along
with this, it might also be useful for the professionals to find the innovative ideas from the study
to lead new inventions in this field.

Business Research Method 5
Research objectives
The research is carried out for determined objectives with relation to the medical industry. The
main objectives of this investigation are as follows
 To investigate the importance of machine learning in medicine
o To develop understanding about concept of machine learning in medicine
o To evaluate examples of application of machine learning in medicine
o To develop the understanding about Brain Age Project as part of applications of
machine learning medicine sector
 To assess the ethical acceptance of machine learning in medicine
o To analyze different ethical issues that are faced with application of machine
learning in medicine sector
o To evaluate the impact of ethical issues or challenges, if not resolved timely with
application of machine learning in medicine sector
o To provide recommendations for overcoming the occurrence of ethical issues and
challenges with application of machine learning in medicine sector
Research questions
The research questions have been designed on basis of devised aims and objectives, which are as
below
 What is the importance of machine learning in the medicine?
o What is the concept of machine learning in medicine sector?
o What are different examples of application of machine learning in medicine
sector?

Business Research Method 6
o What is Brain Age Project as part of applications of machine learning medicine
sector?
 How the ethical aspects of machine learning in medicine can determine?
o What are different ethical issues that are faced with application of machine
learning in medicine sector?
o What can be the consequences of ethical issues or challenges, if they are not
resolved timely with application of machine learning in medicine sector?
o What recommendations can be given for overcoming the occurrence of ethical
issues and challenges with application of machine learning in medicine sector?

Business Research Method 7
Literature Review
To investigate the importance of machine learning in medicine
To develop understanding about concept of machine learning in medicine
According to Meng et al. (2016), machine learning can be defined as the use of computer
enabled algorithms and artificial intelligence for purpose of making important calculations and
interpretations of data with regards to a particular problem. The machine learning can enhance
the ability of human being to make different complex decisions with better level of accuracy. In
contrast to this, Jordan and Mitchell (2015) depict that machine learning stands for the usage of
an automated data analytical model that is taken into account for making different types of
calculations and statistical algorithms. This data analytical model is based on the application of
computer enabled technologies, software and artificial intelligence. One of the key attributes of
machine learning is that it takes into account the minimum level of human intervention in the
calculation, as human is needed for feeding the inputs or raw data for calculations.
To evaluate examples of application of machine learning in medicine
Cufoglu and Coskun (2016) explain that there are different types of new treatments that are
visible in the field of medicine like brain age project. Machine learning can play a vital role in
this type of treatment for finding resolution to the challenges that are faced in today’s ageing
society in the healthcare system. As per a report published by authorities of European Union
Eurostat the growth rate of population is very high. At the same time, life expectancy is also
increasing across the Europe but issues of good health are not increasing with the same rate.
There is need of highly efficient and advance medical support system for purpose of meeting the
increase life expectancy need of people. According to this report, the problem of dementia will

Business Research Method 8
become as a major healthcare problem for future generations. As per observation of Daly and
Walsh (2018), WHO or World Health Organization has provided estimation that the number of
patients with the health issue of dementia will increase to 75 million by end of 2030 and its triple
times by end of 2050. These are certain facts that are already known by the healthcare
professionals. So there is need of finding out some solution to the indicated health issues. The
solution to these problems can only be ascertained through application of advance technologies
and innovations emerging in the field of medicine sector. Machine learning is anticipated as a
primary component that is required for origination of innovations in the field of medicine and
healthcare sector. In contrast to this, the application of artificial intelligence enables the
computers to analyze data, perform different calculations in order to determine specific pattern
available in the data and to provide better decisions to human being.
In the views of Cabitza et al. (2017), machine learning plays an important role in the field of
medicine and medical line. In healthcare sector, the application of machine learning is taken into
account for different purposes of research and development works, conducting diagnosis of
different disease symbols, and for better analysis of treatment outcomes. Example of a company
that is using machine learning technique in the field of medicine is Deep Mind Health of Google.
Core focus of this initiative is to find out solution to problem of macular degeneration in aging
eyes. In support of this, Faggella (2018) comments that machine learning technology is highly
adopted by the healthcare experts for diagnosis and identification of ailments with regards to any
disease in the field of medicine. IBM Watson Genomics is example of an initiative that is
planned and implemented with Quest Diagnostics that has emphasized on integration of genomic
tumor sequencing with the cognitive computing for purpose of making the strides in precision
medicine. Another example of application of machine learning in the field of medicine is Berg

Business Research Method 9
that is a bio-pharma company that is situated in the city of Boston. This company is consistently
using artificial intelligence (or AI) technology for performing the research and diagnosis. This
company is also involved in usage of machine learning technique and AI for therapeutic
treatments in different fields like oncology.
To develop the understanding about Brain Age Project as part of applications of machine
learning medicine sector
Example of application of artificial intelligence in medicine is medical image recognition, which
is mainly based on concept of deep learning. This technology (i.e. deep learning) was emerged in
1960s. These technologies are quite helpful today with the usage of improvements in parallel
data processing, usage of new algorithms, and the better data access. Example of application of
machine learning is visible in the case of Prof. Dr. med. Christian Wachinge. Prof. Wachinge is
the head of laboratory at the Ludwig-Maximilians-Universität (LMU) Munich in the department
of Child and Adolescent Psychiatry (Kharrat et al., 2010). Prof. Wachinge mainly deals in
medical imaging with the application of AI. He has emphasized that the data analytics need in
medical field has opened new opportunities for the computer scientists. In other words, there is
high demand for staff that can perform tasks or responsibilities of analysis and interpretation of
medical records for extraction of meaningful pattern. In University Hospital Munich, Prof.
Wachinge has used the machine intelligence for finding solution to different health issues like
brain abnormalities, and the mental illness (Pardoe et al., 2017). The Brain Age Project is
example of such initiative. This project has been carried out with the uses of SAP. Under this
project, the employees of LMU Munich have been organized into team (i.e. SAP Machine
Learning Team). This team has the goal of identification of new methods or ways for harnessing

Business Research Method 10
the machine learning in order to find business solutions in medicine field through application of
SAP.
Through Brain Age Project, the LMU team was dedicated to support the patients and doctors for
identification of advance treatment methods. Due to the work of LMU team and application of
SAP machine learning, a framework was developed in accordance to age estimation in neuro-
imaging. As a result of work on Brain Age Project, manual interpretation of brain scans resulting
from MRI technology is a time consuming process. In this context, the data obtained from MRI
scans of the healthy volunteers were used for purpose of creating machine learning model for
ascertainment of ageing signs of the brains. This model has proved to be effective for the
physicians for estimation of age of the brain (Cole et al., 2015). The application of deep machine
learning can be highly effective for helping physicians for conducting automatic analysis of brain
structure, even when the patient is on device or diagnosis machine. Apart from these, the usage
of machine learning technology will be effective to reduce the cost of medical care for both
patients and healthcare professionals. But for this, there is need of continuous research in the
field of application of machine learning, AI and big data analytics in medicine.
To assess the ethical acceptance of machine learning in medicine:
To analyze different ethical issues that is faced with application of machine learning in
medicine sector
As per the findings of Char et al. (2018), there are different ethical issues or challenges that may
be encountered with the application of machine learning in medicine field. The usage of machine
learning can only be productive, if the benefits of this technology are realized by healthcare
experts. The human biases in decision making can hamper the productivity of machine learning
technology in medicine sector. Another ethical issue with the application of machine learning is

Business Research Method 11
that there is possibility that repository being created is just the collection of thoughts of minds of
medical experts not the actual risks. It is also an ethical issue that there is possibility of error in
the designing or programming of algorithms to be used in the calculation and analysis. This type
of ethical issue was faced with Uber’s software tool Greyball. Example of a deception was
visible at Volkswagen in which company designed algorithms in a way to allow its vehicle
models to pass the emission tests. This type of algorithm mistakes can make the usage of
machine learning in medicine and health care sector worthless for both doctors and patients.
To evaluate the impact of ethical issues or challenges, if not resolved timely with
application of machine learning in medicine sector:
In accordance to Choy et al. (2018), there are different consequences that may be faced by the
healthcare organizations and health care professionals if the ethical issues or challenges are not
resolved timely. The poor quality issue may be faced by the healthcare professionals and
patients, if the ethical issues arise with the application of machine learning in medicine. For
example, if the machine learning algorithm is designed poorly, the results produced by such
system cannot provide accurate results about health of a patient. This way, the quality of
diagnosis, medication and treatment may hamper as a result of occurrence of ethical issues. In
contrast to this, Obermeyer and Emanuel (2016) state that one of the biggest issue that may be
faced by healthcare experts due to emergence of ethical issues with the application of machine
learning in medicine field. The legal actions may be faced by the organizations and professionals
working in healthcare sector. As a result of this, the huge fines may be faced by the healthcare
organizations and even their license may be dismissed by the regulatory bodies due to such
incidents.

Business Research Method 12
To provide recommendations for overcoming the occurrence of ethical issues and
challenges with application of machine learning in medicine sector:
In the opinions of Erickson et al. (2017), occurrence of ethical issues can be prevented through
high level of training of the people involved in programming and application of machine learning
in medicine. Only the productive and valid data should be taken into account for designing
machine learning algorithms in the automated systems in medical and healthcare sector. In order
to avoid biased decision making, it is very important to issue special directions to the healthcare
staff to follow the results produced by automated healthcare systems. In addition to this
Kickingereder et al. (2016) indicated that the development of repository should be constructed
through inclusion of actual risks not just the thoughts from minds of healthcare professionals.
Apart from this, the internal control system of company should be very powerful that can avoid
occurrence of any deception from internal players of the organization.
Research Methodology Design
The research designing is an important part of the study which supports the researchers in
context of data collection and analyzing the data in relevant manner. The research methodology
is developed for the study to engage into the gathering of information with respect to the
investigation of research problem (Novikov and Novikov, 2013). This research study is carried
out in order to explore the importance of machine learning in the medicine with including the
ethical acceptance of machines in the medicine line.
Research onion
The research onion is developed with the inclusion of research philosophies, designing,
approaches, data collection strategy and the time horizon for the investigation.