Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness an...

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Vydané v:NPJ digital medicine Ročník 4; číslo 1; s. 96 - 14
Hlavní autori: Syrowatka, Ania, Kuznetsova, Masha, Alsubai, Ava, Beckman, Adam L., Bain, Paul A., Craig, Kelly Jean Thomas, Hu, Jianying, Jackson, Gretchen Purcell, Rhee, Kyu, Bates, David W.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 10.06.2021
Nature Publishing Group
Nature Portfolio
Predmet:
692/699/255/2514
692/700/1538
692/700/478/174
Artificial intelligence
Biomedicine
Biotechnology
COVID-19
Digital technology
Health informatics
Medicine
Medicine & Public Health
Pandemics
Public health
Review
Review Article
ISSN:2398-6352, 2398-6352
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Shrnutí:Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.
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ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-021-00459-8