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Editorial
. 2021 Jan;31(1):5-7.
doi: 10.1007/s00330-020-07148-2. Epub 2020 Aug 14.

Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow

Elmar Kotter  1 , Erik Ranschaert  2
Affiliations
Editorial

Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow

Elmar Kotter et al. Eur Radiol. 2021 Jan.
No abstract available

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Conflict of interest statement

The authors of this manuscript declare relationships with the following companies:

Erik Ranschaert is shareholder in Osimis.io

Erik Ranschaert is shareholder in Diagnose.me

References

    1. Willemink MJ, Koszek WA, Hardell C et al (2020) Preparing medical imaging data for machine learning. Radiology. 10.1148/radiol.2020192224 - PMC - PubMed
    1. Prevedello LM, Halabi SS, Shih G et al (2019) Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions. Radiology: Artificial Intelligence. 10.1148/ryai.2019180031 - PMC - PubMed
    1. Recht MP, Dewey M, Dreyer K et al (2020) Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations. Eur Radiol. 10.1007/s00330-020-06672-5 - PubMed
    1. Sim Y, Chung MJ, Kotter E et al (2019) Deep convolutional neural network-based software improves radiologist detection of malignant lung nodules on chest radiographs. Radiology. 10.1148/radiol.2019182465 - PubMed
    1. Geis JR, Brady AP, Wu CC et al (2019) Ethics of artificial intelligence in radiology: summary of the joint European and north American multisociety statement. Insights Imaging. 10.1186/s13244-019-0785-8 - PMC - PubMed

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