Module 1: Fundamentals of Radiology
Essential concepts and imaging modalities for AI applications
Introduction to Diagnostic Imaging
Radiology is a medical specialty that uses imaging techniques to diagnose and treat diseases. The field has evolved dramatically since the discovery of X-rays by Wilhelm Röntgen in 1895, with modern radiology encompassing a wide range of imaging modalities.
Key Learning Objectives
- Understand the historical development of radiology
- Recognize the basic principles of different imaging techniques
- Identify the role of AI in modern radiological practice
The Evolution of Radiology
The field of radiology has undergone remarkable transformation over the past century:
- 1895: Discovery of X-rays by Wilhelm Röntgen
- 1970s: Introduction of computed tomography (CT)
- 1980s: Development of magnetic resonance imaging (MRI)
- 1990s-2000s: Digital radiography and PACS systems
- 2010s-Present: Integration of AI and machine learning
Figure 1: Historical timeline of major developments in radiology
The Diagnostic Imaging Process
Modern radiological practice involves several key steps:
- Image Acquisition: Capturing the medical images using appropriate modality
- Image Processing: Enhancing and optimizing images for interpretation
- Image Interpretation: Analysis by radiologists to identify abnormalities
- Reporting: Documentation of findings and diagnostic impressions
- Communication: Sharing results with referring physicians and patients
AI's Emerging Role
Artificial intelligence is transforming each step of the imaging process:
- • Optimizing image acquisition protocols
- • Enhancing image quality and reconstruction
- • Automated detection and characterization of abnormalities
- • Prioritization of urgent findings
- • Structured reporting and decision support