Explainable artificial intelligence in pathology

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 demo...

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Bibliographic Details
Published in:Pathologie (Heidelberg, Germany) Germany), 2024-03, Vol.45 (2), p.133-139
Main Authors: Klauschen, Frederick, Dippel, Jonas, Keyl, Philipp, Jurmeister, Philipp, Bockmayr, Michael, Mock, Andreas, Buchstab, Oliver, Alber, Maximilian, Ruff, Lukas, Montavon, Grégoire, Müller, Klaus-Robert
Format: Article
Language:ger
Subjects:
Artificial Intelligence
Hope
Pathology, Molecular
Precision Medicine
Online Access:Get full text
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Summary: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).
ISSN:2731-7196
DOI:10.1007/s00292-024-01308-7