Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective

Rachel N. Flach; Nina L. Fransen; Andreas F.P. Sonnen; Tri Q. Nguyen; Gerben E. Breimer; Mitko Veta; Nikolas Stathonikos; Carmen van Dooijeweert; Paul J. van Diest

doi:10.3390/diagnostics12051042

Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective

Rachel N. Flach, Nina L. Fransen, Andreas F.P. Sonnen, Tri Q. Nguyen, Gerben E. Breimer, Mitko Veta, Nikolas Stathonikos, Carmen van Dooijeweert, Paul J. van Diest^*

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

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Abstract

Building on a growing number of pathology labs having a full digital infrastructure for pathology diagnostics, there is a growing interest in implementing artificial intelligence (AI) algorithms for diagnostic purposes. This article provides an overview of the current status of the digital pathology infrastructure at the University Medical Center Utrecht and our roadmap for implementing AI algorithms in the next few years.

Original language	English
Article number	1042
Pages (from-to)	1-11
Journal	Diagnostics
Volume	12
Issue number	5
DOIs	https://doi.org/10.3390/diagnostics12051042
Publication status	Published - 21 Apr 2022

Keywords

artificial intelligence
digital pathology
implementation
machine learning
roadmap

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10.3390/diagnostics12051042Licence: CC BY

diagnostics-12-01042Final published version, 2.17 MBLicence: CC BY

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title = "Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective",

abstract = "Building on a growing number of pathology labs having a full digital infrastructure for pathology diagnostics, there is a growing interest in implementing artificial intelligence (AI) algorithms for diagnostic purposes. This article provides an overview of the current status of the digital pathology infrastructure at the University Medical Center Utrecht and our roadmap for implementing AI algorithms in the next few years.",

keywords = "artificial intelligence, digital pathology, implementation, machine learning, roadmap",

author = "Flach, {Rachel N.} and Fransen, {Nina L.} and Sonnen, {Andreas F.P.} and Nguyen, {Tri Q.} and Breimer, {Gerben E.} and Mitko Veta and Nikolas Stathonikos and {van Dooijeweert}, Carmen and {van Diest}, {Paul J.}",

note = "Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.",

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T2 - The UMC Utrecht Perspective

AU - Flach, Rachel N.

AU - Fransen, Nina L.

AU - Sonnen, Andreas F.P.

AU - Nguyen, Tri Q.

AU - Breimer, Gerben E.

AU - Veta, Mitko

AU - Stathonikos, Nikolas

AU - van Dooijeweert, Carmen

AU - van Diest, Paul J.

PY - 2022/4/21

Y1 - 2022/4/21

N2 - Building on a growing number of pathology labs having a full digital infrastructure for pathology diagnostics, there is a growing interest in implementing artificial intelligence (AI) algorithms for diagnostic purposes. This article provides an overview of the current status of the digital pathology infrastructure at the University Medical Center Utrecht and our roadmap for implementing AI algorithms in the next few years.

AB - Building on a growing number of pathology labs having a full digital infrastructure for pathology diagnostics, there is a growing interest in implementing artificial intelligence (AI) algorithms for diagnostic purposes. This article provides an overview of the current status of the digital pathology infrastructure at the University Medical Center Utrecht and our roadmap for implementing AI algorithms in the next few years.

KW - artificial intelligence

KW - digital pathology

KW - implementation

KW - machine learning

KW - roadmap

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U2 - 10.3390/diagnostics12051042

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M3 - Review article

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Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective

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