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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

Future artificial intelligence tools and perspectives in medicine.

Ahmad Chaddad¹, Yousef Katib², Lama Hassan¹

¹School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China.

Current Opinion in Urology

|April 30, 2021

Summary

This summary is machine-generated.

Artificial intelligence (AI) enhances medical diagnosis through radiomic analysis of imaging data. Deep learning models show promise for improving AI-driven cancer diagnosis, even with limited datasets.

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiomics

Background:

Artificial intelligence (AI) is increasingly utilized in medicine as a clinical support tool, particularly for computer-aided diagnosis.
AI tools analyze diverse medical data, including images, molecular data, and clinical variables, using statistical and machine-learning methods.
Radiomic analysis, a key AI application, extracts quantitative features from medical images for predictive modeling, potentially avoiding invasive biopsy procedures.

Purpose of the Study:

To review and discuss the latest radiomic pipelines for clinical analysis.
To explore the advancements in AI-based radiomic tools for clinical applications.
To explain new radiomic approaches utilizing deep learning techniques.

Main Methods:

Summarizing recent radiomic pipelines used in clinical analysis.
Exploring AI-based radiomic tools and their technical steps.
Describing deep radiomic analysis leveraging deep convolutional neural networks.

Main Results:

AI-driven radiomic analysis offers noninvasive predictive modeling from imaging data.
Deep learning techniques, such as deep convolutional neural networks, can enhance radiomic models.
AI radiomic tools show potential for application even with limited datasets.

Conclusions:

Further investigation into radiomic algorithms is recommended.
Incorporating deep learning into radiomic models requires additional validation across various cancer types.
AI-based radiomic analysis holds significant promise for advancing cancer diagnosis and management.