Specialisation: Article Review

Artificial Intelligence (Article Review)


This essay’s purpose is to critically analyze an article that focuses on artificial intelligence. Specifically, the analysis will focus on some of the most critical aspects of the article, including a review of the article’s objectives, strengths and weaknesses, a conclusion, and various recommendations. The study titled “Artificial intelligence in healthcare: past, present, and future” was written by Jiang et al. (2017) and is the subject of this review. In this section, the authors discuss how artificial intelligence (AI) has recently made significant waves in the healthcare industry, even kindling a debate regarding whether or not AI doctors may eventually replace human physicians. They argue that the role of doctors will not necessarily be taken over by artificial intelligence in the foreseeable future. However, it will assist them in making better clinical decisions, and in some areas of the industry, such as radiology, it may even fully replace human judgment (Jiang et al., 2017). According to the information that was provided in the article, the implementation of AI in the healthcare industry has been made possible by the growing availability of healthcare data as well as the quick development of methodologies for big data analysis (Jiang et al., 2017). When prompted by the appropriate clinical questions, robust artificial intelligence systems have the ability to discover therapeutically valuable information that is concealed in vast volumes of data. For this reason, the clinical decision-making process can benefit from having access to this information.

Summary of the Article (Objective Review)

The researchers who contributed to this article examined the transformative effects that the development of artificial intelligence has had on the field of healthcare and how it has evolved over the past few decades. In particular, they cited the escalating availability of medical data and the quickening pace of methodological advancement as two examples (Jiang et al., 2017). Despite this, researchers in 2017 did not have a clear understanding of the existing and future applications of AI in the healthcare industry. As a direct consequence of this fact, the primary objective of the authors of the article was to investigate the current state of artificial intelligence applications in the healthcare industry as well as their potential for the future. An additional, more in-depth inquiry into the applications of AI in stroke was carried out by the researchers. The researchers were focused on three primary areas: early detection and diagnosis, therapy, and outcome prediction and prognosis evaluation.

Strengths and Weaknesses of the Article


The most valuable aspect of this article is that it analyzed and discussed the most common AI tools for machine learning and natural language processing as potential solutions. This is the article’s greatest strength and the most important addition that the paper makes to the field. The techniques for machine learning can be further broken into two categories: the more standard approaches and the more cutting-edge deep learning techniques. As another key focus of the study, artificial intelligence applications in neurology could be presented and discussed in the article as well (Jiang et al., 2017). These applications were investigated from the perspectives of early disease diagnosis and prediction, therapy, prognosis evaluation and outcome prediction all of which contribute to this field of research.


The fact that there was an inadequate amount of data interchange is the primary weakness of the study. In order for artificial intelligence (AI) systems to work properly, they need to undergo consistent training using data obtained from clinical trials. The availability of new data is essential for the continued development and improvement of an AI system after it has been initially trained on historical information and has been put into operation. This is because AI systems learn best from examples that are similar to real-world situations. As a result of the fact that the existing healthcare system does not provide individuals with any kind of incentive to share their data on the system, the research was quite difficult to do.

Conclusion and Recommendations

In conclusion, the paper has reviewed the article by focusing on the primary areas of interest. According to the authors of this article, AI can be used effectively to improve healthcare by analyzing a wide variety of healthcare data sets. This article also provides an overview of the various illness categories AI has been used to cure. After that, the authors went into depth on the two primary classifications of AI tools: machine learning (ML) and natural language processing (NLP). In machine learning, the researchers concentrated on the two methods that have shown to be the most successful historically: neural networks and support vector machines (SVM). After that, they analyzed the three primary classifications of AI applications in stroke care.

It is essential, in my opinion, to conduct some research on artificial intelligence in specific sections of a department in order to have a deeper understanding of AI’s significant role in the operations of that particular department. It is more beneficial for researchers to research the topic rather than try to think of any new ideas. The currently available software and hardware linked with AI to advance the department will make it easier for humans to manage their lives.


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 neurology2(4).