When consumers are first exposed to AI in the medical realm, their first response is hesitation, and rightfully so. Humans’ natural tendency is to process and know the system potentially being performed on them before having it perform a task on them. Longoni, Bonezzi, and Morewedge discuss different studies that illustrate human tendencies and the problem of consumer public approval in nine studies. These points are backed up by data points and research. In study 6, Longoni discusses how artificial intelligence has been neglected by the public to perform its works. Consumers believe that artificial intelligence is not able to see the severity of responsibility. In addition, artificial intelligence also lacks the capacity of having human-like emotions, which is crucial in the workforce. After these were conducted on humans, the results showed that the overall uniqueness of these systems has too many questions than answers to be trusted.
This study will work perfectly with my exploratory essay because it conducts studies showing how artificial intelligence’s limitations make it hard for the public to approve of this system. In this article, the authors compare and contrast different ways artificial intelligence can be used. Machine learning does not only include learning algorithms but being specialized in different sections of medicine. Alsuliman, Humaidan, and Sliman also discuss how artificial intelligence, not necessarily in the medical realm, has been growing exponentially. It addresses more on the positive side of artificial intelligence than the previous entry. Artificial intelligence has a vast variety of applications like radiology (science dealing with radioactive sensory tools), hematology (advanced study in the blood), neurology (anatomy science), and etc.
The use of hematology goes in-depth about how crazy visualizations using technology can test bloodstreams levels in patients to see results. We will focus on one specialized area in how artificial intelligence helps disorders using laser technology. Through many cases using AI labeled as the HbE (Alsuliman 1) and IDA (Alsuliman 1) which has been connected to Thalassemia, a blood disorder in humans, it has been proven that artificial intelligence is accurate. This will help my essay by showing how artificial intelligence can potentially revolutionize the world as it surpasses humans in this aspect. One of the studies showed that this system has 91% of efficiency when checking samples. This can also apply to my essay by proving that the future of artificial intelligence is dark in some aspects but also looks bright. This article examines how artificial intelligence is changing health care as well as physicians applications.
The article discusses how many professors and case studies, present the impact of artificial intelligence on the medicine world. They continue to dive in-depth about the promises that artificial intelligence has to this day. Artificial intelligence has a low threshold, but the studies show that the good outweighs the bad. Some promises have already been accomplished by artificial intelligence which includes documentation of healthcare in a faster, less chance of error way. In one of their specializations, cardiology, artificial intelligence has been shown far more than beneficial. It can be used to prevent the risk of cardiovascular disease by detecting signs earlier. Due to a vast number of studies, they also predicted out of a number of patients how many would have a heart attack due to family history.
This can be used in my paper as showing it can help prevent and find solutions for diseases as well as predict outcomes before an event of a health problem. Artificial intelligence has been proven to be hurtful in many aspects, but there are good to weigh the bad. This article is more unique than the others as it connects artificial intelligence to drug discovery. Artificial intelligence is thought to be beneficial in drug discovery as well as resistance. It can be used to identify different drugs and prescribe the right one to a patient. However, there is a problem with this as people react in different manners to different medications, but they are proven to try to answer the questions of reliability. Furthermore, artificial intelligence has gotten a lot of praise in recent years and especially now with drug discovery and cancer treatment. In one of the sections called, “Artificial Intelligence Methods Applied to Identify Variants/Mutations from Genetic Data” (Nagarajan 1), one of the systems called convolutional neural networks is to signify when different genes are mutated.
This machine is proven to be prevalent in today’s world as it uses an algorithm tested by physicians to get results. This applies to my paper in an amazing way as computational/artificial intelligence can be used to help patients out in finding the right drug. Sometimes physicians prescribe the wrong drug due to insufficient knowledge of research or sometimes human error, but with AI this will limit both of those factors. With this new finding, the sky’s the limit to how these systems can be used for cancer resistance and drug discovery. The article starts by addressing current questions that people might have about artificial intelligence. Then it transitions into some quick history about the first discovery and the upbringing of computers. Akl and Salay address how there are different aspects in artificial intelligence currently like facial recognition and even photographs captions.
The rest of the article then dives deeper into providing evidence and reason to see if artificial intelligence has a bright future or not. One of the aspects they looked into was the algorithms machine learning takes and the strengths/flaws from it. Are robots trustworthy? Will artificial intelligence ever meet or surpass the needs of the public? Will a computer gain the capacity to completely overtake humans’ jobs’ positions or does it have a ceiling before then? These are all legitimate questions asked by Akl and Salay in this article. The authors suggest in these paragraphs that artificial intelligence does have a bright future with human-like knowledge, advances in emotional behavior like humans, and better algorithms for even patients with different needs/wants in the procedure.
This applies to my paper well as it provides a great foundation to show yes AI does have questions, but they are shown to have answers.This ebook goes in depth of its perception and facts that show that artificial intelligence has shown an increase in unemployment in the world. Artificial intelligence has been beneficial in tons of ways, but it has made a lot of people scrambling for a job. It lastly talks about how we as humans can ensure that not all of our jobs will be taken away. One of the sections of the ebook called “Technological Unemployment” (Korinek 29) actually shows how unemployment pertains in artificial intelligence instead of the obvious fact of humans losing jobs.
Since artificial intelligence jobs are usually work-intensive but not done by humans which creates massive wear and tear, balancing wages between the two become very complicated. It causes only two scenarios in which a balance has to be restored or lowering wages significantly for one party has to take place in order to achieve fairness all around. If fairness even was achieved between majority AI workplaces and majority human workplaces, redistributions are complicated because people have not come up with a solution to this dilemma. This ties perfectly into my paper because I want to show how artificial intelligence impacts the economy as well as the advances it has placed. Also, it shows that artificial intelligence is again bigger than the workforce as it impacts the entire world.
I see a common theme of the sources reflecting back on caste studies to support their stances. I have also seen that these articles/journals/e-books have touched on not only the positive but the negative as well to either refute or support. The common gap I see in my research is the lack of connectors between artificial intelligence and different social classes. I have also begun to realize that my research lacks necessary information about growth over the years of AI in medicine.
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