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Artificial Intelligence and Machine Learning for Digital Pathology pp 1–15Cite as

Expectations of Artificial Intelligence for Pathology

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Abstract

Within the last ten years, essential steps have been made to bring artificial intelligence (AI) successfully into the field of pathology. However, most pathologists are still far away from using AI in daily pathology practice. If one leaves the pathology annihilation model, this paper focuses on tasks, which could be solved, and which could be done better by AI, or image-based algorithms, compared to a human expert. In particular, this paper focuses on the needs and demands of surgical pathologists; examples include: Finding small tumour deposits within lymph nodes, detection and grading of cancer, quantification of positive tumour cells in immunohistochemistry, pre-check of Papanicolaou-stained gynaecological cytology in cervical cancer screening, text feature extraction, text interpretation for tumour-coding error prevention and AI in the next-generation virtual autopsy. However, in order to make substantial progress in both fields it is important to intensify the cooperation between medical AI experts and pathologists.

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Acknowledgements

The authors declare that there are no conflicts of interests and the work does not raise any ethical issues. Parts 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 program under grant agreements No 824087 “EOSC-Life” and No 826078 “Feature Cloud”. We thank the anonymous reviewers for their critical but helpful comments.

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Authors and Affiliations

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

    Peter Regitnig & Heimo Müller

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

    Heimo Müller & Andreas Holzinger

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  1. Peter Regitnig
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Correspondence to Peter Regitnig .

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  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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Regitnig, P., Müller, H., Holzinger, A. (2020). Expectations of Artificial Intelligence for Pathology. 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_1

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