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Updated: Jul 18, 2026

Artificial Intelligence Approaches to Assessing Primary Cilia
Published on: May 1, 2021
[Explainable artificial intelligence in pathology]
Frederick Klauschen1,2,3,4, Jonas Dippel5,6, Philipp Keyl7
1Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland. frederick.klauschen@med.uni-muenchen.de.
Abstract:
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).
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