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将人工智能融入新冠疫情和未来大流行的床边护理中。

Integrating artificial intelligence in bedside care for covid-19 and future pandemics.

机构信息

Center for Integration and Analysis of Medical Data (CITADEL), Department of Medicine, University of Montreal, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada.

Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM), Canada.

出版信息

BMJ. 2021 Dec 31;375:e068197. doi: 10.1136/bmj-2021-068197.

DOI:10.1136/bmj-2021-068197
PMID:34972694
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8719330/
Abstract

examine the challenges in developing AI tools for use at point of care

摘要

探讨在开发用于即时护理的人工智能工具方面所面临的挑战

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[1]
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[2]
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[6]
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[7]
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[8]
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[9]
In the September 2021 Issue of the Quarterly.

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[10]
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[7]
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本文引用的文献

[1]
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence.

BMJ Open. 2021-7-9

[2]
Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?

BMJ. 2021-3-15

[3]
Using AI ethically to tackle covid-19.

BMJ. 2021-3-15

[4]
Invited Commentary: Treatment Drop-in-Making the Case for Causal Prediction.

Am J Epidemiol. 2021-10-1

[5]
Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines.

Trials. 2021-1-6

[6]
The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies.

J Biomed Inform. 2021-1

[7]
AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.

Med Image Anal. 2020-10-15

[8]
Transparency and reproducibility in artificial intelligence.

Nature. 2020-10

[9]
A short guide for medical professionals in the era of artificial intelligence.

NPJ Digit Med. 2020-9-24

[10]
The myth of generalisability in clinical research and machine learning in health care.

Lancet Digit Health. 2020-8-24

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