Considerations for development and use of AI in response to COVID-19

Sipior, Janice C.

doi:10.1016/j.ijinfomgt.2020.102170

Cited by 104 publications

(55 citation statements)

References 8 publications

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“…Technologies powered by artificial intelligence (AI) including machine learning, image recognition, and deep learning algorithms can be used for early detection and diagnosis of the infection, more rapid drug discovery for developing new treatments ( Brohi et al, 2020 ). A few companies also repurposed existing AI systems that were initially designed for other areas to assist in social distancing enforcement and contract tracing ( Sipior, 2020 ).…”

Section: Existing It Solutionsmentioning

confidence: 99%

Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic

Zhang

2021

International Journal of Information Management

349

199

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Various technology innovations and applications have been developed to fight the coronavirus pandemic. The pandemic also has implications for the design, development, and use of technologies. There is an urgent need for a greater understanding of what roles information systems and technology researchers can play in this global pandemic. This paper examines emerging technologies used to mitigate the threats of COVID-19 and relevant challenges related to technology design, development, and use. It also provides insights and suggestions into how information systems and technology scholars can help fight the COVID-19 pandemic. This paper helps promote future research and technology development to produce better solutions for tackling the COVID-19 pandemic and future pandemics.

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Section: Existing It Solutionsmentioning

confidence: 99%

Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic

Zhang

2021

International Journal of Information Management

349

199

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“…[1] [2] Non-iterative weighted Weibull showed an average MAPE of 12 percent greater than iteratively weighted Weibull when applied to the same dataset. Algorithm 1 provides a step-bystep theory for iteratively weighted curve fitting using the GIW distribution (called "Robust Weibull").…”

Section: Selection Of the Distribution Modelmentioning

confidence: 99%

Mortality Prediction of COVID-19 Pandemic Using Artificial Intelligence

Dwivedi

Upadhyay

Pal

2020

2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)

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Artificial Intelligence deals with the machines or a systems that understand, learn think and behave like humans do. Recent research in Artificial Intelligence mainly focused on developing an intelligent system for detecting, observing and analysing to create a more personalised patient experience. Nowadays mortality prediction is one of the crucial research area which aims to save hospital resources and patients money. The mortality rate is used to compare the overall seriousness of illness between groups of critically ill patients and between clinical trial groups of patients. Documented data has been used in this study which are taken from websites of World Health Organization (WHO) and www.ourworldindata.org/ coronavirus-source-information. This paper presents a mortality prediction model of based on Artificial Intelligence to help hospitals to decide which patient needs care and attention first. The result demonstrates 90% accuracy in anticipating the rate of mortality. We used Levenberg-Marquardt (LM) technique and Robust Curve Fitting using Iterative weighting.

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“…In [37] some of many considerations for managing the development of AI applications including planning, unpredictable, unexpected or biased results, re-purposing, the importance of data and diversity in AI team membership is addressed. The author provide implications for research and for practice according to each of the considerations.…”

Section: Introductionmentioning

confidence: 99%