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Updated: Dec 12, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Artificial Intelligence in Pathology: A Simple and Practical Guide.

Keluo Yao¹, Amol Singh², Kaushik Sridhar¹

¹Department of Pathology, University of California, San Francisco, San Francisco.

Advances in Anatomic Pathology

|August 11, 2020

Summary

This summary is machine-generated.

Artificial intelligence (AI) is transforming pathology by enabling computers to automate tasks. This guide explains AI methods like digital image analysis and natural language processing for pathologists.

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Area of Science:

Pathology
Computational Pathology
Medical Informatics

Background:

Artificial intelligence (AI) is increasingly influencing pathology.
Computational techniques enable computers to perform tasks traditionally done by humans.
Understanding AI is becoming crucial for pathology professionals.

Purpose of the Study:

To provide a practical guide to AI methods in pathology.
To review key AI techniques including digital image analysis, next-generation sequencing, and natural language processing.
To offer historical context and future perspectives on AI in pathology.

Main Methods:

Comprehensive literature review of AI applications in pathology.
Explanation of core AI methodologies relevant to pathology.
Development of a glossary of AI terminology.

Main Results:

Detailed overview of AI methods applicable to pathology.
Discussion on the historical evolution and future trajectory of AI in the field.
A tabular dictionary defining essential AI terms for pathologists.

Conclusions:

AI offers significant potential to enhance and automate tasks in pathology.
This guide serves as a foundational resource for pathologists and researchers engaging with AI.
Familiarity with AI terminology and methods is essential for the future of pathology practice.