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Updated: Jan 25, 2026
10:17
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
Published on: January 8, 2018
13.6K
Deep learning and radiomics in precision medicine
Vishwa S Parekh1,2, Michael A Jacobs1,3
1The Russell H. Morgan Department of Radiology and Radiological Sciences, John Hopkins University, School of Medicine, Baltimore, MD, USA.
Summary
Artificial intelligence, deep learning, and radiomics are transforming radiology for personalized patient diagnoses. These advanced technologies are set to unite, revolutionizing clinical decision support in precision medicine.
Area of Science:
- Radiology
- Computer Science
- Precision Medicine
Background:
- The integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) with radiomics represents a paradigm shift in radiological practice.
- AI and radiomics aim to enhance the definition of tissue characteristics for personalized patient diagnoses.
Purpose of the Study:
- To provide a comprehensive review of historical and current deep learning and radiomics methodologies.
- To explore the application of these technologies within the framework of precision medicine in radiology.
Main Methods:
- A systematic literature search was conducted across major scientific databases (PubMed, ArXiv, Scopus, etc.).
- Keywords included 'Deep Learning', 'Radiomics', 'Machine learning', 'Artificial Intelligence', 'Convolutional Neural Network', 'Generative Adversarial Network', 'Autoencoders', 'Deep Belief Networks', 'Reinforcement Learning', and 'Multiparametric MRI'.
Main Results:
- Deep learning and radiomics are rapidly advancing fields with significant potential in medical imaging.
- The review synthesizes current research on AI, DL, and radiomics applications in radiology.
Conclusions:
- Deep learning and radiomics are poised to merge into a unified framework for clinical decision support.
- This integration has the potential to revolutionize precision medicine in radiology.
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