The Integration of Radiomics and Artificial Intelligence in Modern Medicine

Area of Science:

• Radiology
• Medical Imaging
• Artificial Intelligence

Background:

• Radiomics extracts quantitative features from medical images, providing valuable biomarkers.
• Artificial intelligence (AI) is transforming contemporary medicine, particularly in patient care.
• AI techniques like machine learning and deep learning are key to advanced radiomics applications.

Purpose of the Study:

• To review the diverse applications of AI in radiomics.
• To explore AI's role in quantitative feature extraction, diagnostic systems, and predictive modeling.
• To discuss the impact of AI in radiomics on workflow, clinical trials, and patient stratification.

Main Methods:

• Review of AI techniques (machine learning, deep learning) applied to radiomics.
• Analysis of AI's role in computer-aided diagnostic (CAD) systems.
• Examination of multimodal integration and advanced deep learning architectures.

Main Results:

• AI significantly enhances quantitative feature extraction and analysis in radiomics.
• AI enables sophisticated CAD systems, predictive models, and decision support tools.
• AI integration improves workflow efficiency, clinical trial optimization, and patient stratification.

Conclusions:

• AI in radiomics holds immense potential for improving diagnostic accuracy.
• AI facilitates personalized treatment strategies and better patient outcomes.
• Regulatory and clinical adoption of AI-based radiomics requires careful consideration.