Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review
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MOBILE & WIRELESS HEALTH
Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review Agam Bansal 1 & Rana Prathap Padappayil 2 & Chandan Garg 3 & Anjali Singal 4 & Mohak Gupta 5 & Allan Klein 6 Received: 8 June 2020 / Accepted: 15 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls. Keywords COVID 19 . Artificial intelligence . Machine learning . Outcome prediction
Introduction As the global population grows, even though we are still battling some of the pathogens that have been with us since the advent of known human history such as tuberculosis, we are also witnessing a trend in increasing emergence of novel pathogens from non-human hosts, and this is posing a major threat to the public health [1]. Within the last ten years, we witnessed the emergence of new viruses that could potentially spread across international borders and wreak global havoc, the latest of this being the novel coronavirus (COVID-19). The recent development of machine learning-based tools for healthcare
providers allows novel ways to combat such global pandemics. The term machine learning encompasses the collection of tools and techniques for identifying patterns in data [2]. In traditional methods of identifying patterns from data, we approach the system with our presumptions as to which components of the data (age, sex, pre-existing conditions) affect the outcome of interest (patient survival). However, in machine learning, we provide data and the machine identifies trends and patterns, enabling us to formulate a model to predict the outcome of patients. The authors will attempt to provide a narrative review of such tools, how they are useful in healthcare, and how they are being utilized in the prediction,
Agam Bansal and Rana Prathap Padappayil contributed equally to this manuscript. This article is part of the Topical Collection on Mobile & Wireless Health * Allan Klein [email protected] Agam Bansal [email protected] Rana Prathap Padappayil [email protected] Chandan Garg [email protected] Anjali Singal [email protected]
Mohak Gupta [email protected] 1
Internal Medicine, Cleveland Clinic, Cleveland, OH, USA
2
Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA, USA
3
Deptartment of Statistics, Columbia Un
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