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[Artificial intelligence for pathology-how, where, and why?]

Peter Schüffler1,2,3, Katja Steiger4, Carolin Mogler4

  • 1Institut für Pathologie, TUM School of Medicine and Health, Technische Universität München, München, Deutschland. peter.schueffler@tum.de.

Pathologie (Heidelberg, Germany)
|March 13, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers pathology innovations but presents challenges. This overview covers AI's current status, achievements, and remaining hurdles in pathology.

Keywords:
AutomationDigital medicineDigital pathologyQuality assuranceQuality control

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Area of Science:

  • Pathology
  • Medical Informatics
  • Artificial Intelligence

Context:

  • The integration of artificial intelligence (AI) into pathology workflows is rapidly advancing.
  • Pathology faces numerous questions and uncertainties regarding AI implementation and efficacy.

Purpose:

  • To provide a concise overview of the current landscape of AI in pathology.
  • To highlight the achievements of existing AI algorithms in diagnostic and analytical tasks.
  • To identify and discuss the challenges and limitations that need to be addressed for broader AI adoption.

Summary:

  • Current AI applications in pathology demonstrate significant progress in automating tasks and improving diagnostic accuracy.
  • Key achievements include enhanced image analysis, predictive modeling, and workflow optimization.
  • Despite successes, challenges remain in areas such as data standardization, algorithm validation, and clinical integration.

Impact:

  • This review aims to inform pathologists, researchers, and developers about the state of AI in pathology.
  • Understanding current capabilities and challenges is crucial for guiding future research and development.
  • Facilitating informed decision-making regarding the adoption and implementation of AI tools in clinical pathology settings.