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Review
. 2024 Mar;45(2):133-139.
doi: 10.1007/s00292-024-01308-7. Epub 2024 Feb 5.

[Explainable artificial intelligence in pathology]

[Article in German]
Frederick Klauschen  1   2   3   4 , Jonas Dippel  5   6 , Philipp Keyl  7 , Philipp Jurmeister  7   8 , Michael Bockmayr  9   10   11 , Andreas Mock  7   8 , Oliver Buchstab  7 , Maximilian Alber  9   12 , Lukas Ruff  12 , Grégoire Montavon  5   6   13 , Klaus-Robert Müller  14   15   16   17   18
Affiliations
Review

[Explainable artificial intelligence in pathology]

[Article in German]
Frederick Klauschen et al. Pathologie (Heidelb). 2024 Mar.

Abstract
in English, German

With the advancements in precision medicine, the demands on pathological diagnostics have increased, requiring standardized, quantitative, and integrated assessments of histomorphological and molecular pathological data. Great hopes are placed in artificial intelligence (AI) methods, which have demonstrated the ability to analyze complex clinical, histological, and molecular data for disease classification, biomarker quantification, and prognosis estimation. This paper provides an overview of the latest developments in pathology AI, discusses the limitations, particularly concerning the black box character of AI, and describes solutions to make decision processes more transparent using methods of so-called explainable AI (XAI).

Mit den Entwicklungen der Präzisionsmedizin steigen die Anforderungen an die pathologische Diagnostik, histomorphologische und molekularpathologische Daten standardisiert, quantitativ und integriert zu beurteilen. Große Hoffnungen werden in Verfahren der Künstlichen Intelligenz (KI) gesetzt, die gezeigt haben, komplexe klinische, histologische und molekulare Daten zur Krankheitsklassifikation, Biomarkerquantifizierung und Prognoseabschätzung auswerten zu können. Diese Arbeit gibt einen Überblick über neueste Entwicklungen der KI in der Pathologie, diskutiert die Grenzen insbesondere hinsichtlich der Intransparenz der KI und beschreibt Lösungen, die Entscheidungsprozesse mit Verfahren der sog. erklärbaren KI („explainable AI“, XAI) transparenter zu gestalten.

Keywords: Artificial intelligence; Biomarkers; Machine learning; Molecular biology; Precision medicine.

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References

Literatur

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