SIT740 - Research and Development in IT: AI Literature Review Analysis

This report is a mini literature review on the topic of artificial intelligence and machine learning, focusing on the application domain of image processing and radiomics, as well as the challenges of implementing machine learning in organizations. The review analyzes four research papers, discussing their key concepts, methodologies, and outcomes. The report contrasts and compares the papers, noting similarities and differences in the problems addressed, approaches taken, outcomes, and technology used. It also highlights the development of ideas across the four papers, including experiments, results, limitations, advantages, and disadvantages. The conclusion summarizes key ideas and suggests potential areas for future research in the field. The report also reviews the role of machine learning in different fields and support financial specialist.

Running head: ARTIFICIAL INTELLIGENCE
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Name of the Student
Name of the university
Author note

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Introduction
Artificial intelligence is simulation of human intelligence in the machines and these
machines have been developed with certain programming that enables them to think like their
human counterpart and mimic human actions as well. Machines exhibit traits that include
learning as well as problem solving. In this paper some of the aspects of artificial intelligence
will be discussed on the basis of four articles, the concepts of the authors and their view on
the same.
Concepts and ideas
Machine learning is an emerging concept which is made use of by computer systems
to perform and conduct particular task. Challenges that can be encountered while using
machine learning are also to be considered (Hatt, Parmar, Qi and El 2019). In case of
machine learning the researchers are of the opinion that in order to successfully implement
this technology, organizations need to overcome certain challenges. The concepts of machine
learning and their use in different organizations are a matter of concern. There are instances
where machine learning provides wrong outcomes to the users and yields errors in the results
thus affecting the accuracy (Wexler 2019). The researchers are worried about the challenges
mainly because in order to adopt this technology these should be mitigated or handled
properly. The common challenges are inaccessible data, inflexible business models, sensitive
data security and affordability of the organisations.
Contrast and comparison of the articles reviewed
The first article considers different methods for image processing as well as
radiomics. The role of machine learning is to be understood as well as the challenges. The
other articles are on challenges that organizations face for adopting machine learning

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(Cherman, Papanikolaou, Tsoumakas & Monard 2019). The methods used in data collection
are different in the articles. Some authors have considered primary methods while others have
made use of secondary or qualitative analysis. In one of the articles the way in which AI can
be programmed to carry out devastating tasks have also been analyzed (Li and Du 2017).
Machines cannot be relied upon all the time and thus while some articles consider it as an
advanced technology, others consider the challenges in the same.
Conclusion
From the above discussion it can be concluded that machine learning should be
adopted by organizations so that they can advance with the new technology but at the same
time there is a need to understand the challenges in the same so that these can be handled
accordingly. AI can be beneficial if used in a positive way but can also be used for
destructive purposes.

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References
Cherman, E.A., Papanikolaou, Y., Tsoumakas, G. & Monard, M.C., 2019. Multi-label active
learning: key issues and a novel query strategy. Evolving Systems , 10(1), pp.63-78.
Hatt, M., Parmar, C., Qi, J. and El Naqa, I., 2019. Machine (deep) learning methods for
image processing and radiomics. IEEE Transactions on Radiation and Plasma Medical
Sciences, 3(2), pp.104-108.
IEEE transactions on visualization and computer graphics, 26(1), pp.56-65.
Li, D. and Du, Y., 2017. Artificial intelligence with uncertainty. CRC press.
Wexler, J. et al., 2019. The What-If Tool: Interactive Probing of Machine Learning Models.

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