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Explainable artificial intelligence in pathology

Frederick Klauschen
Jonas Dippel
Philipp Keyl
Philipp Jurmeister
Michael Bockmayr
Andreas Mock
Oliver Buchstab
Maximilian Alber
Lukas Ruff
Grégoire Montavon & Klaus-Robert Müller

February 05, 2024

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).