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Related Experiment Video

Updated: Aug 28, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:30

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

Published on: July 11, 2025

197

Multi-modality artificial intelligence in digital pathology.

Yixuan Qiao1,2, Lianhe Zhao1, Chunlong Luo1,2

  • 1Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.

Briefings in Bioinformatics
|September 20, 2022
PubMed
Summary
This summary is machine-generated.

Digital pathology, powered by artificial intelligence (AI), offers a solution to slow medical test results. AI analyzes whole slide images for faster, standardized diagnoses, improving healthcare resource allocation.

Keywords:
deep learningdigital pathologymulti-modalitywhole slide images

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Area of Science:

  • Digital pathology
  • Computational pathology
  • Medical imaging analysis

Background:

  • Traditional diagnostic methods face challenges with time-consuming and costly test result generation.
  • Digital pathology leverages computational tools to enhance data management and diagnostic efficiency.
  • Artificial intelligence (AI) shows significant promise in accelerating the data analytics phase of pathology.

Approach:

  • This review focuses on hematoxylin-eosin stained tissue slide images, a common data type in pathology.
  • The study explores the integration of AI algorithms with high-throughput sequencing for multi-modal data analysis.
  • Deep learning technology is investigated for its role in assisting medical professionals.

Key Points:

  • AI algorithms can provide standardized and up-to-date conclusions from whole slide images.
  • AI enables the analysis of morphological features alongside gene expression data.
  • The potential of AI in addressing healthcare resource disparities is examined.

Conclusions:

  • AI and digital pathology offer strategic solutions to improve diagnostic efficiency and healthcare resource allocation.
  • Deep learning advancements present opportunities for AI to support clinicians.
  • The review discusses the challenges and prospects of implementing AI in pathology.