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[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.

Pathologie (Heidelberg, Germany)
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Summary
This summary is machine-generated.

Artificial intelligence (AI) in pathology enhances precision medicine by analyzing complex data for diagnosis and prognosis. Explainable AI (XAI) is crucial for transparently interpreting these AI-driven pathological assessments.

Keywords:
Artificial intelligenceBiomarkersMachine learningMolecular biologyPrecision medicine

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Area of Science:

  • Pathology
  • Medical Diagnostics
  • Artificial Intelligence

Context:

  • Precision medicine requires advanced pathological diagnostics.
  • Histomorphological and molecular data integration is essential.
  • Current diagnostic methods face increasing demands.

Purpose:

  • Overview of recent advancements in Artificial Intelligence (AI) for pathology.
  • Discuss limitations of AI in pathology, focusing on the 'black box' problem.
  • Introduce Explainable AI (XAI) as a solution for transparent AI decision-making.

Summary:

  • AI methods analyze complex clinical, histological, and molecular data for disease classification, biomarker quantification, and prognosis.
  • AI demonstrates significant potential in enhancing pathological diagnostics.
  • Explainable AI (XAI) addresses the transparency challenge in AI pathology.

Impact:

  • Improved accuracy and standardization in pathological diagnostics.
  • Facilitates more reliable disease classification and prognosis estimation.
  • Enhances trust and clinical adoption of AI tools in pathology.