Intelligent Monitoring and Diagnosing Capability in Healthcare: Systematic Literature Review

Abstract

Recently, Artificial Intelligence (AI) development has become more advanced and has been extensively used in various kinds of necessities in healthcare, supporting medical workers in many cases. AI can be used for monitoring and diagnosing because of the AI's ability to find solutions by using patterns that have been trained from datasets. Thus, this research reviewed the related research papers about AI role in the healthcare field, which focused on monitoring and diagnosing diseases. This study collected many relevant studies from various academic database sources, such as Google Scholar, Springer, IEEE Xplore, PubMed, Semantic Scholar, and Scopus Elsevier by using PRISMA 2009 (Preferred Reporting Items of Systematic reviews and Meta-Analyses) guidance. Our review discovered that AI can be helpful in certain areas of expertise, such as infectious disease using algorithms such as neural networks, fuzzy clustering networks, and Naïve Bayesian network; cancer using algorithms such as SVM, regression, and random forest classifier; COVID-19 using algorithms such as Naïve Bayesian Network, 3D CNN segmentation models, and ResNet-based models; and other diseases. In this review, the implementation of AI in healthcare brings both benefits and drawbacks. From the benefits side, AI can be very impactful by enhancing patient treatment and reducing costs. Meanwhile, on the downside, AI has some problems, such as a lack of information that makes the AI model not represent the real data. Because of that, AI will not replace medical workers in five years but only fill the role of support for them in monitoring and diagnosing, allowing them to concentrate on the most critical areas. Based on result analysis, assorted needs appear to exist to upgrade the implementation of AI in the healthcare field to support human health resources. This research suggests that AI researchers should be focused on specific fields in the healthcare field, especially in the diagnosing and monitoring fields, to find new insights.

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