Core curriculum · Module 2 of 3
Radiology Basics: Images, Systems & Reports
How images are made, what they are to a computer, how a scan moves through the hospital, and how to read the report. One patient’s story runs through all four lessons. If you already read images, you can skim or skip this module.
- Core question: What is radiology, and how does a scan become data?
- 4 lessons
- 15-20 min each
- No radiology background needed
Lessons
Each lesson follows Mr. Diaz, a patient with a suspected blood clot in the lungs, from the first scan to the report. Where AI plugs into this workflow is covered in Lesson 3.1.
- 2.1
What Radiology Is
AvailableThe five main imaging tools, why things look white or black, slices and planes, and the radiation and contrast questions to ask before a scan.
- 2.2
The Journey of a Scan
AvailableOne patient’s CT from order to follow-up: the EHR, RIS and PACS, why the reason for exam matters, critical results, and where errors happen.
- 2.3
Images as Data
AvailablePixels and CT numbers, the DICOM file and its header, windowing, image quality and artifacts, and how de-identified images become AI datasets.
- 2.4
Reading a Radiology Report
AvailableThe parts of a report, impression versus findings, hedging language, RADS categories, and explaining a report to a patient in plain words.
Going further
Optional, and separate from the core lessons.
AI infrastructure & MLOps. Deployment architecture, inference, APIs, containers, cloud and on-premises environments, model versioning and technical monitoring. Useful for research and implementation leaders; covered in the Research Lab. See the technical deep dive →
Where this module fits
Three modules, then the assessment. Research Lab and Leadership are optional tracks you can open at any time.