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. 2021;110(1):1-14.
doi: 10.1007/s10994-020-05928-x. Epub 2020 Dec 9.

How artificial intelligence and machine learning can help healthcare systems respond to COVID-19

Mihaela van der Schaar  1   2 , Ahmed M Alaa  2 , Andres Floto  1 , Alexander Gimson  3 , Stefan Scholtes  1 , Angela Wood  1 , Eoin McKinney  1 , Daniel Jarrett  1 , Pietro Lio  1 , Ari Ercole  1   3
Affiliations

How artificial intelligence and machine learning can help healthcare systems respond to COVID-19

Mihaela van der Schaar et al. Mach Learn. 2021.

Abstract

The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide personalized patient management plans, inform policy, and expedite clinical trials. In this paper, we introduce five of the most important challenges in responding to COVID-19 and show how each of them can be addressed by recent developments in machine learning (ML) and artificial intelligence (AI). We argue that the integration of these techniques into local, national, and international healthcare systems will save lives, and propose specific methods by which implementation can happen swiftly and efficiently. We offer to extend these resources and knowledge to assist policymakers seeking to implement these techniques.

Keywords: COVID-19; Clinical decision support; Healthcare.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
High-level illustration of how the proposed ML and AI solutions can help healthcare systems respond to COVID-19
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References

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    1. Alaa, A. M., & van der Schaar, M. (2020). Frequentist uncertainty in recurrent neural networks via blockwise influence functions.

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