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Related Concept Videos

Anatomical Terminology01:20

Anatomical Terminology

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Knowledge of anatomy is essential to understand human biology and medicine. Anatomists and health care professionals use standard terminology to describe the human body with more precision and no ambiguity. Anatomical terms have mostly Greek and Latin-derived roots. Because these languages are rarely used in conversation, the meaning of words remains the same. Each term is made up of a root in between the prefixes and suffixes. The root of a term often refers to an organ, tissue, or condition,...
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Simplified Artificial Intelligence Terminology for Pathologists.

Fatemeh Zabihollahy1,2, Michael Mankaruos1, Maxim Mohareb1

  • 1Laboratory Medicine Program, University Health Network, Toronto, ON M5G 2C4, Canada.

Diagnostics (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances digital pathology diagnostics. This review simplifies AI concepts for pathologists, improving communication and showcasing AI

Keywords:
Artificial intelligencecomputer-aided diagnosisdeep learningdigital pathologyfoundation modelsmachine learning

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Medical artificial intelligence

Background:

  • Digital pathology adoption is increasing globally.
  • Artificial intelligence (AI) offers potential for enhanced diagnostics in pathology.
  • Bridging the communication gap between AI scientists and pathologists is essential.

Purpose of the Study:

  • To simplify AI terminology for pathologists.
  • To provide practical examples and illustrations of AI concepts.
  • To foster better communication between AI scientists and pathologists.

Main Methods:

  • Review of AI technologies and algorithms in computational pathology.
  • Explanation of frameworks for training AI models.
  • Discussion of image analysis nomenclature and public datasets.

Main Results:

  • Provides a comprehensive overview of AI in computational pathology.
  • Explains common AI terms and concepts in an accessible manner.
  • Highlights the advantages and resources for AI implementation in pathology.

Conclusions:

  • Effective communication is key to integrating AI in digital pathology.
  • Understanding AI concepts empowers pathologists to leverage its benefits.
  • This review serves as a foundational resource for pathologists entering the field of AI-driven pathology.