Deliverable 3 - Evaluate Research and Data
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Deliverable 3 - Evaluate Research and Data
Attempt 2
Jamie Raines
Rasmussen College
HSA5000CBE Section 01CBE Scholarly Research and Writing
Caroline Gulbrandsen
9/1/2022
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Research Question Evaluation
The Credibility of the Data
The research question integrated into this study is related to how artificial intelligence (AI)
integration in clinical radiology has the potential to disrupt the industry. According to Becker et al.
(2022), AI is strongly connected to the operations performed in clinical radiology for better test
results. The technology allows machines to achieve human-level performance while detecting
tumors during radiology tests. There are suitable improvements performed in the AI industry using
research that structures technical operations by machines to validate the Integration of AI
algorithms for patient care. The credibility of the data in the article is reliable since the authors
researched how healthcare professionals who have used artificial intelligence have promoted better
health management. The European Society of Radiology has accredited all the authors due to their
degrees and advanced educational levels (Becker et al., 2022). In the other article, the authors also
integrate credibility since workers have experience in hospitals and are at a university level of
education (Mulryan et al., 2022). Most research participants agreed that AI integration in radiology
information technology (IT) departments has promoted accuracy and reduced excess time for
setting up systems.
The next article focused on the use of AI for medical imaging, whereby it is clear that the
demand for AI is constantly progressing. According to Mulryan et al. (2022), the advancements in
AI have been adverse, causing some radiologists to develop a negative attitude towards the
industry that could potentially eliminate human jobs. AI has been found to simulate human brain
capacity, which does not get received well by professionals in healthcare settings. This indicates
more operations can get performed to validate AI operations since they are needed for system
management. The article's data was collected from journalists, radiologists, commercial
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representatives, researchers, and non-radiologist doctors, and all provided different opinions on the
impact of AI in medical imaging. Other health experts also provided their knowledge as long as
they received advanced education for their professions.
Documentation of the Data
All the articles integrate a high-quality data documentation process since there are different
topics, use of graphics, and graphs that explain how the research was conducted. There was a direct
process to determine that the articles were quantitative since there was a comparison among
different variables. In the article by Becker et al. (2022), the data analyzed how different
respondents reacted to the value of AI in clinical radiology. The data was then recorded in tables
under different questions so that there would be an accurate analysis outcome by finding out the
number of responses that supported or did not support concepts mentioned in a research question.
In the same article, an example of accurate documentation is a graph that indicated data from
clinical radiologists on why they did not acquire certified AI-algorism expertise.
In the second article, Mulryan et al. (2022) offered accurate data representations using
schematics that integrated different reactions and the number of participants by applying a logistic
regression model to determine the data's validity. There was an accurate quantitative research
method by analyzing how different participants were used in the research and how their answers
got distributed. The study was direct and only required participants to answer questions during
survey sessions while providing their data for easy identification. Collecting data from different
radiologists was applied to promote the study's reliability since the data can get assessed by any
professional and produce a health management standard.
Evaluation of Data Analysis and Interpretation
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The data collected from the articles support a hypothesis developed for the study:
conforming to AI practices in radiology is imperative since they inevitably affect healthcare
delivery. The future of AI appears to be getting more advanced, especially for the radiology
industry, which integrates a structure for change in handling system operations. It can be possible
to handle superintelligence operations based on AI's ability to mimic human clinical radiologists'
behavior. Becker et al. (2022) introduced expertise in handling AI operations advancements based
on the capability to correlate ideas for generating better data analysis. The existence of AI for
clinical radiology is a structure connected to the change of different aspects in the healthcare
environment due to its capability to manage system operations. There is thus a structure for change
in terms of better machine operations for patient healthcare improvement. Management of human
beings' acceptance of AI is required for the best procedure of offering intelligence improvement for
a safe healthcare future (Mulryan et al., 2022). These authors adopted a document analysis process
by seeking credible sources on how radiology operations get performed.
Possible Ethical Issues
In conclusion, it would be possible to correlate real-life positive healthcare outcomes and
the data provided by persons familiar with the area of interest. There are conditions required to
improve AI operations, including algorithm management and logic handling, which are imperative
to support healthcare expertise by clinical radiologists as they learn to perform AI operations.
While performing any personal study, there can be a constricted method when attempting to
understand how to seek factual data without the integration of plagiarism of original information.
Another issue can be obtaining informed consent from professionals in the radiology industry. It is
easy to find final work posted online, yet communicating with the developers and ensuring they
allow their work to be used in research can be challenging. Confidentiality is another requirement
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that needs to be analyzed to seek information on how to protect the original owners of any piece of
work without appearing to steal data. All these possible issues can be addressed by conducting
thorough research and assessing the required topic.
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References
Becker, C., Kotter, E., Fournier, L., & Martí-Bonmatí, L. (2022). Current practical experience with
artificial intelligence in clinical radiology: a survey of the European Society of Radiology.
Insights Into Imaging, 13(1). doi: 10.1186/s13244-022-01247-y.
Mulryan, P., Ni Chleirigh, N., O’Mahony, A., Crowley, C., Ryan, D., & McLaughlin, P. et al.
(2022). An evaluation of information online on artificial intelligence in medical
imaging. Insights Into Imaging, 13(1). doi: 10.1186/s13244-022-01209-4
