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Towards a Better Understanding of the Workflows: Modeling Pathology Processes in View of Future AI Integration

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Artificial Intelligence and Machine Learning for Digital Pathology

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12090))

Abstract

A profound understanding of the pathology processes is an essential precondition for successful introduction of changes and innovations, such as for example AI and Machine Learning, into pathology. Process modeling helps to build up such a profound understanding of the pathology processes among all relevant stakeholders. This paper describes the state of the art in modeling pathology processes and shows on an example how to create a reusable multipurpose process model for the diagnostic pathology process.

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Acknowledgements

The authors declare that there are no conflicts of interests and the work does not raise any ethical issues. Part of this work has been funded by the Austrian Science Fund (FWF), Project: P-32554 “A reference model of explainable Artificial Intelligence for the Medical Domain”, and by the European Union’s Horizon 2020 research and innovation programme under grant agreements No 824087 “EOSC-Life” and No 826078 “Feature Cloud”.

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Authors and Affiliations

  1. Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria

    Michaela Kargl, Heimo Müller & Andreas Holzinger

  2. Diagnostic and Research Institute of Pathology, Medical University Graz, Neue Stiftingtalstraße 6, 8036, Graz, Austria

    Peter Regitnig & Heimo Müller

Authors
  1. Michaela Kargl
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  2. Peter Regitnig
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  3. Heimo Müller
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  4. Andreas Holzinger
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Corresponding author

Correspondence to Michaela Kargl .

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Editors and Affiliations

  1. Medical University of Graz, Graz, Austria

    Andreas Holzinger

  2. University of Alberta, Edmonton, AB, Canada

    Randy Goebel

  3. University of Alberta, Edmonton, AB, Canada

    Michael Mengel

  4. Medical University of Graz, Graz, Austria

    Heimo Müller

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Kargl, M., Regitnig, P., Müller, H., Holzinger, A. (2020). Towards a Better Understanding of the Workflows: Modeling Pathology Processes in View of Future AI Integration. In: Holzinger, A., Goebel, R., Mengel, M., Müller, H. (eds) Artificial Intelligence and Machine Learning for Digital Pathology. Lecture Notes in Computer Science(), vol 12090. Springer, Cham. https://doi.org/10.1007/978-3-030-50402-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-50402-1_7

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