Erklärbare Künstliche Intelligenz in der Pathologie

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

doi:10.1007/s00292-024-01308-7

Erklärbare Künstliche Intelligenz in der Pathologie

Translated title of the contribution: 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^*

^*Corresponding author for this work

Department of Artificial Intelligence

Research output: Contribution to journal › Article › peer-review

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).

Translated title of the contribution	Explainable artificial intelligence in pathology
Original language	German
Pages (from-to)	133-139
Number of pages	7
Journal	Pathologie
Volume	45
Issue number	2
DOIs	https://doi.org/10.1007/s00292-024-01308-7
Publication status	Published - 2024 Mar

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2024.

Keywords

Artificial intelligence
Biomarkers
Machine learning
Molecular biology
Precision medicine

ASJC Scopus subject areas

Pathology and Forensic Medicine

Access to Document

10.1007/s00292-024-01308-7

Cite this

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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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doi = "10.1007/s00292-024-01308-7",

language = "German",

volume = "45",

pages = "133--139",

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T1 - Erklärbare Künstliche Intelligenz in der Pathologie

AU - Klauschen, Frederick

AU - Dippel, Jonas

AU - Keyl, Philipp

AU - Jurmeister, Philipp

AU - Bockmayr, Michael

AU - Mock, Andreas

AU - Buchstab, Oliver

AU - Alber, Maximilian

AU - Ruff, Lukas

AU - Montavon, Grégoire

AU - Müller, Klaus Robert

N1 - Publisher Copyright: © The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2024.

PY - 2024/3

Y1 - 2024/3

N2 - 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).

AB - 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).

KW - Artificial intelligence

KW - Biomarkers

KW - Machine learning

KW - Molecular biology

KW - Precision medicine

UR - https://www.scopus.com/pages/publications/85184185081

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DO - 10.1007/s00292-024-01308-7

M3 - Article

C2 - 38315198

AN - SCOPUS:85184185081

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SP - 133

EP - 139

JO - Pathologie

JF - Pathologie

IS - 2

ER -

Erklärbare Künstliche Intelligenz in der Pathologie

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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