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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
Summary
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.
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.
