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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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Clinician's perception of healthcare artificial intelligence

Authors: Choudhury, Avishek;

Clinician's perception of healthcare artificial intelligence

Abstract

This survey is only designed for medical professionals (doctor/nurse/physician/etc). You were selected as a possible participant in this study because of your expertise and experience in the medical field. Since artificial intelligence is growing and is likely to be an integral part of healthcare in the future, perceptions, and suggestions of clinicians is crucial as it will guide respective governing bodies and researchers develop and integrate AI into the clinical workflow without disturbing clinicians' and more importantly to provide them with assistance. This short online survey is designed to understand clinicians' overall perception of AI-based clinical decision support systems. The study will help identify clinicians' expectancy from AI in their respective fields of expertise and their willingness to use AI in their routine clinical practice. The information that will be obtained from this study shall help human factor scientists design clinician-centered AI decision support systems. In this survey, please consider AI-based clinical decision support systems as an automated system that mimics human cognition and generates results based on past data to assist you (healthcare worker) in decision making. For example, automated clinical decision support system (often embedded in electronic medical records), automated diagnosis using images (mobile app for skin cancer detection), machine learning-based prediction models (patient survival prediction, patient readmission, length of stay, etc.), and others.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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0
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26
21
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Cancer Research