MGT502 Module 3.1: Artificial Intelligence in Healthcare Report

This report provides a comprehensive overview of the current and future applications of artificial intelligence (AI) in healthcare. It analyzes the integration of AI technologies, such as machine learning and deep learning, across various aspects of medicine, including diagnostics, patient care, and medical research. The report examines the benefits of AI, such as improved accuracy, efficiency, and accessibility in healthcare delivery, while also addressing the challenges related to data privacy, algorithm transparency, and ethical considerations. The analysis includes a review of multiple research articles, highlighting key findings and perspectives on AI's impact on healthcare professionals, patients, and the overall healthcare industry. The report explores specific applications like AI-powered diagnostic tools, virtual biopsies, and the use of AI in addressing the shortage of healthcare staff. It further discusses the ethical implications and regulatory environments surrounding AI in healthcare, providing a well-rounded perspective on the present and future of AI in the medical field.

Running head: ARTIFICIAL INTELLIGENCE IN HEALTH CARE
Artificial Intelligence in Healthcare
Name of the Student:
Name of the University:
Author note:

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Article 1: He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The
practical implementation of artificial intelligence technologies in medicine. Nature
medicine, 25(1), 30.
As per the author of this article, the growth and development of the artificial
intelligence (AI) based technologies in the field of medicine is gradually advancing and that
too, with a fast pace. However, in the real world the clinical implementation of the same has
not yet become a reality and it will take more time to have successful implementation of the
same. He et al. (2019) in this context have reviewed some of the significant practical issues
that are surrounding the implementation and use of artificial intelligence into the prevailing
or current clinical workflows, comprising od privacy, date sharing, data standardisation,
transparency of the algorithms as well as the interoperability all over different platforms in
terms of patients’ safety. The paper has further summarised the existing regulatory
environment in U.S.A as well as highlighted the situation by comparing it with the other parts
of the world like China and Europe.
Article 2: Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A.,
Prikhodko, P., ... & Ogu, I. O. (2018). Converging blockchain and next-generation
artificial intelligence technologies to decentralize and accelerate biomedical research
and healthcare. Oncotarget, 9(5), 5665.
Mamoshina et al. (2018) in this article have opined that the digital revolution in the
field of medicine has successfully produced a significant shift in the current healthcare
industry. As per the article, the ever increased availability and accessibility of the data along
with the recent developments in AI have presented some unprecedented chances and
opportunities in the healthcare industry and with the same, some major challenges for the
regulators, patients, providers and developers The transfer of learning techniques and the

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novel deep learning techniques are turning the data of the patients into medical data and at the
same time, are also transforming the simple pictures and videos into some powerful sources
of data for the predictive analytics. The article have provided a brief overview of the “next
generation artificial intelligence” and the block chain technologies and have presented some
unique and creative solutions that could be used for accelerating the biomedical research with
the help of AI.
Article 3: Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017).
Artificial intelligence in healthcare: past, present and future. Stroke and vascular
neurology, 2(4), 230-243.
Jiang et al. (2017) in this article has highlighted the benefits that artificial intelligence
(AI) has provided to the healthcare industry. The main aim of AI is mimicking the human
cognitive functions. It is bringing in a paradigm shift to the healthcare industry and it is
powered by some increasing availability of the healthcare data and by rapid progressing of
the analytics techniques. The present status of AI application in the medical field is also
surveyed in order to discuss about the efficiency of it in the future. As per the authors,
artificial intelligence could be applied to different type of healthcare data like the structured
and the unstructured. Some of the popular artificial intelligence techniques are machine
learning methods for the structured data like the neural network, classical support vector
machine etc. It is also claimed in this regard that some of the major disease areas like
neurology, cardiology and cancer today are also making use of AI tools and systems like IBM
Watson.
Article 4: Furmankiewicz, M., Sołtysik-Piorunkiewicz, A., & Ziuziański, P. (2014, July).
Artificial intelligence systems for knowledge management in e-health: the study of

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intelligent software agents. In Latest Trends on Systems: The Proceedings of 18th
International Conference on Systems, Santorini Island, Greece (pp. 551-556).
The authors of this article have described the state of art of the e-health AI systems as
well as have described and compared the different features of the multi-agent systems in the
field of e-health. The article is based on the different theoretical models of the multi-agent
systems for the process of knowledge management in the organisations like presence of
authorities, information allocation, motivating and organisational culture and norms. It is also
to note that artificial intelligence in the field of medicine is the main focus of this article and
as per the authors, it has two main branches and they are- physical and virtual. The physical
branch is represented by the robots that are used for assisting the attending surgeon and the
elderly patient. On the other hand, the virtual branch comprise of the informatics approaches
from the deep learning information management for controlling the health management
systems like active guidance of physicians and electronic health records.
Article 5: Yang, J. J., Li, J., Mulder, J., Wang, Y., Chen, S., Wu, H., ... & Pan, H.
(2015). Emerging information technologies for enhanced healthcare. Computers in
industry, 69, 3-11.
The main aim of this article is to assess the advantages that the emerging information
technologies have provided to the medical industry to enhance the healthcare. As per Yang et
al. (2015), proper consumption and collection of the electronic health information regarding a
patient or a population is a bedrock for the modern healthcare system where the EMR
(Electronic medical records) serve as the main carrier. However, it is also claimed that the
current medical information technologies undoubtedly offer enough opportunities and
possibilities but at the same time, pose certain challenges as well. The trends in the health
sensing, the cloud computing and the big data analysis indeed show some interesting new

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development and enhance the healthcare abilities while boosting the preventive care and
fostering collaborative healthcare. Still, an integral approach is important to be there in
practice and it must be ensured that the important aspects like privacy protection and security
are abided.
Article 6: Hamet, P., & Tremblay, J. (2017). Artificial intelligence in
medicine. Metabolism, 69, S36-S40.
Hamet and Tremblay (2017) in this article have defined artificial intelligence as “is a
general term that implies the use of a computer to model intelligent behaviour with minimal
human intervention”. As per this article, the concept of AI is accepted as having started with
the invention of robots. The main focus of this article is to assess the role played by AI in the
field of medicine. AI has been claimed to be improving the human diagnosis system as the AI
equipped product has the potential of sifting through the disease data, medical records,
clinical studies as well as the health records of the patients too (Hamet & Tremblay, 2017). It
is also serving the rural communities to a great extent where the access to the specialists and
doctors could be tough.
Article 7: Wong, T. Y., & Bressler, N. M. (2016). Artificial intelligence with deep
learning technology looks into diabetic retinopathy screening. Jama, 316(22), 2366-2367.
As per this article, the “push of artificial intelligence into the health care arena is
timely, well-comed and much needs as all the available resources would be required to
address the most pressing health care issues globally in an efficient as well as cost-effective
manner.” It is also to mention that AI is poised for becoming a transformational force in the
health care industry. Although making use of the computers to communicate is not a new idea
but creating some direct interfaces in between human mind and technology without needing
keyboards, monitors and mouse is a cutting edge area of research that has some notable

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applications for some modern patients. Artificial intelligence has enabled the next generation
of tools of radiology that are proper, accurate and detailed for replacing the requirement for
tissue samples in some of the cases. It is also claimed that AI has is also helping in mitigating
the impacts of severe deficit of the qualified clinical staff by means of taking over some of
the diagnostic duties that were once typically allocated for the human beings only.
Article 8: Vogenberg, F. R., & Santilli, J. (2018). Healthcare trends for 2018. American
Health & Drug Benefits, 11(1), 48.
As per this article by Vogenberg and Santilli (2018) have claimed that there are a total
of eight themes that reveal some key healthcare trends and they are rural healthcare,
consumerism in healthcare, workforce change, the administration transformation, integrated
care for the population health, government, technology acceleration and transformative
market impacts and supply chain disruption. These key trends are affecting the contemporary
healthcare marketplace. It is claimed that AI is assisting in enabling virtual biopsies as well as
in advancing the innovative field of radionics that lays emphasis on harnessing the image-
based algorithms for characterising the phenotypes as well as the genetic properties of the
tumours (Vogenberg & Santilli, 2018). With the same, AI tools are widely used for screening
chest x-rays for identifying the signs of tuberculosis and they are often used for achieving the
degree of accuracy that are comparable to the human beings.
Article 9: Furmankiewicz, M., Sołtysik-Piorunkiewicz, A., & Ziuziański, P. (2014).
Artificial Intelligence and Multi-agent software for e-health Knowledge Management
System. Informatyka Ekonomiczna, 2(32), 51-63.
In this article, Furmankierwiczoltysik-Piorunkiewicz and Ziuzianski (2014) have
described and compared the different features using examples of the MAS (multi-agent
systems) in the field of e-health. Various descriptions of MAS in the e-health are illustrated in

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four different areas and they are- assistive living application, physical telemonitoring,
diagnosis, smart emergency application and smart hospital application (Furmankierwicz,
Soltysik-Piorunkiewicz & Ziuzianski, 2014). Artificial intelligence in this article is defined as
“a scientific discipline, emerged after the introduction of the first computers”. The authors of
this article have divided it because of the areas of the supported knowledge management in e-
health and they are- “(1) knowledge about the patient: K4CARE, U-R-SAFE, MyHeart,
MobiHealth, (2) knowledge of the presented medical problem: OHDS, HealthAgents, IHKA,
(3) contextual knowledge about the course of the conversation: CASIS, AID-N, CASCOM,
Akogrimo and (4) knowledge of the health organization: ERMA, SAPHIRE.”
Article 10: Russell, S., Hauert, S., Altman, R., & Veloso, M. (2015). Ethics of artificial
intelligence. Nature, 521(7553), 415-416.
According to this article, AI and robotics communities face some significant ethical
decision like “whether to support or oppose the development of lethal autonomous weapons
systems (LAWS).” It is to note that LAWS are the third revolution in warfare after nuclear
arms and gunpowder. The existing components of AI and robotics could provide some
physical platforms, motor control, perception, tactical decision making, mapping, navigation
and long term planning. For improving the contemporary healthcare system, AI in the
medicine is an effective idea that could advance the patient communication as well as the
healthcare professionals. They successfully enhance the potential of processing and storing
huge amounts of data in intelligent way and translating those information into some
functional tools (Russell et al., 2015). The article has concluded that AI is a great boom for
the healthcare industry as it helps in authentic augmenting clinical judgement, training and
experience along with cost reduction.

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References:
Furmankiewicz, M., Sołtysik-Piorunkiewicz, A., & Ziuziański, P. (2014, July). Artificial
intelligence systems for knowledge management in e-health: the study of intelligent
software agents. In Latest Trends on Systems: The Proceedings of 18th International
Conference on Systems, Santorini Island, Greece (pp. 551-556).
Furmankiewicz, M., Sołtysik-Piorunkiewicz, A., & Ziuziański, P. (2014). Artificial
Intelligence and Multi-agent software for e-health Knowledge Management
System. Informatyka Ekonomiczna, 2(32), 51-63.
Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36-
S40.
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical
implementation of artificial intelligence technologies in medicine. Nature
medicine, 25(1), 30.
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial
intelligence in healthcare: past, present and future. Stroke and vascular
neurology, 2(4), 230-243.
Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., ... &
Ogu, I. O. (2018). Converging blockchain and next-generation artificial intelligence
technologies to decentralize and accelerate biomedical research and
healthcare. Oncotarget, 9(5), 5665.

8ARTIFICIAL INTELLIGENCE IN HEALTHCARE
Russell, S., Hauert, S., Altman, R., & Veloso, M. (2015). Ethics of artificial
intelligence. Nature, 521(7553), 415-416.
Vogenberg, F. R., & Santilli, J. (2018). Healthcare trends for 2018. American Health & Drug
Benefits, 11(1), 48.
Wong, T. Y., & Bressler, N. M. (2016). Artificial intelligence with deep learning technology
looks into diabetic retinopathy screening. Jama, 316(22), 2366-2367.
Yang, J. J., Li, J., Mulder, J., Wang, Y., Chen, S., Wu, H., ... & Pan, H. (2015). Emerging
information technologies for enhanced healthcare. Computers in industry, 69, 3-11.