About: The challenges of deploying artificial intelligence models in a rapidly evolving pandemic

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: The challenges of deploying artificial intelligence models in a rapidly evolving pandemic Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	The challenges of deploying artificial intelligence models in a rapidly evolving pandemic
Creator	Hurst, John Hawkes, David Hu, Yipeng Jacob, Joseph Jm Parker, Geoffrey Stoyanov, Danail
Source	ArXiv
abstract	The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks. The gaze of these networks has in recent years turned increasingly towards applications in healthcare. It was perhaps inevitable that COVID-19, a global disease propagating health and economic devastation, should capture the attention and resources of the world's computer scientists in academia and industry. The potential for AI to support the response to the pandemic has been proposed across a wide range of clinical and societal challenges, including disease forecasting, surveillance and antiviral drug discovery. This is likely to continue as the impact of the pandemic unfolds on the world's people, industries and economy but a surprising observation on the current pandemic has been the limited impact AI has had to date in the management of COVID-19. This correspondence focuses on exploring potential reasons behind the lack of successful adoption of AI models developed for COVID-19 diagnosis and prognosis, in front-line healthcare services. We highlight the moving clinical needs that models have had to address at different stages of the epidemic, and explain the importance of translating models to reflect local healthcare environments. We argue that both basic and applied research are essential to accelerate the potential of AI models, and this is particularly so during a rapidly evolving pandemic. This perspective on the response to COVID-19, may provide a glimpse into how the global scientific community should react to combat future disease outbreaks more effectively.
has issue date	2020-05-19(xsd:dateTime)
bibo:doi	10.1038/s42256-020-0185-2
has license	arxiv
sha1sum (hex)	b90482e2b4c30aa9f2329f361b4f09bd07145630
schema:url	https://doi.org/10.1038/s42256-020-0185-2
resource representing a document's title	The challenges of deploying artificial intelligence models in a rapidly evolving pandemic
resource representing a document's body	covid:b90482e2b4c30aa9f2329f361b4f09bd07145630#body_text
is schema:about of	named entity 'gaze' named entity 'The' named entity 'surveillance' named entity 'observation' named entity 'surprising' named entity 'healthcare' named entity 'disease' named entity 'Models' named entity 'ECONOMY' named entity 'HAVE' named entity 'EPIDEMIC' named entity '5B1' named entity 'DIFFERENT' named entity 'This' named entity 'essential' named entity 'The' named entity 'artificial intelligence' named entity 'moving' named entity 'evolving' named entity 'health' named entity 'economic' named entity 'response' named entity 'discovery' named entity 'transformed' named entity 'disease outbreaks' named entity 'COVID' named entity 'COVID' named entity 'AI models' named entity 'scientific community' named entity 'antiviral drug' named entity 'eventual eradication' named entity 'AI models' named entity 'AI models' named entity 'radiographs' named entity 'intensive care unit' named entity 'AI models' named entity 'intubation' named entity 'disease outbreaks' named entity 'data collection' named entity 'COVID' named entity 'COVID' named entity 'prognosis' named entity 'big data' named entity 'model training' named entity 'CT images' named entity 'epidemic' named entity 'machine learning' named entity 'mechanical ventilators' named entity 'AI models' named entity 'FDA' named entity 'COVID' named entity 'virus' named entity 'AI models' named entity 'mechanical ventilation' named entity 'COVID' named entity 'AI models' named entity 'data sets' named entity 'nasopharyngeal' named entity 'RT-PCR' named entity 'scientific community' named entity 'sequelae' named entity 'long-term complications' named entity 'computed tomography' named entity 'exponential growth' named entity 'AI models' named entity 'COVID-19 pandemic' named entity 'data management' named entity 'cytokine release syndrome' named entity 'epidemic' named entity 'AI models' named entity 'clinical imaging' named entity 'COVID-19' named entity 'COVID-19 pandemic' named entity 'ethical issues' named entity 'training data' named entity 'Reverse transcription polymerase chain reaction' named entity 'point-of-care ultrasound' named entity 'AI models' named entity 'overfitting' named entity 'coronavirus pandemic'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software