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Sector roadmap

Radiology & Medical Imaging

Diagnostic and interventional radiology: modality choice, structured reporting, imaging informatics and AI, and image-guided therapy.

RSNAACRDICOMBI-RADS
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Default learning track

4 phases · ~16–22 weeks

A baseline path through this sector with milestones, prerequisites, and concrete projects. Click Personalize this roadmap above to have the AI tailor pace, depth, and resources to your background and goals.

1

Phase 1 — Foundations

3–4 weeks 30–40 hours

Build the conceptual and quantitative base needed to read papers and follow tutorials in the sector.

Prerequisites

  • Comfortable with basic biology / health-science vocabulary
  • Ability to install software and use a terminal

Skills you'll build

Python or R basicsLinux shellGit/GitHubReading scientific literature

Hands-on projects

Reproducible analysis notebook

Walk through a published tutorial in DICOM + 3D Slicer + Python and reproduce its results on the provided sample data.

Deliverable: GitHub repo with a Jupyter/Quarto notebook, environment.yml, and README

Milestones

  • Set up reproducible Conda/Mamba environment and a public GitHub repo
  • Read and summarize 3 review papers covering the sector landscape
  • Complete an intro statistics or scripting course end-to-end
Citation · documentation
UpToDate clinical reference
Wolters Kluwer
Why: Category match (Medicine & Healthcare)
Dataset · dataset
PubMed
NCBI
Why: Category match (Medicine & Healthcare)
2

Phase 2 — Core tools & datasets

4–6 weeks 60–80 hours

Learn the standard analytical stack of the sector and the canonical public datasets used by professionals.

Prerequisites

  • Completed Phase 1 reproducible notebook
  • Working Python/R environment

Skills you'll build

DICOM + 3D Slicer + PythonData wranglingQC & exploratory analysisVersion-controlled pipelines

Hands-on projects

Dataset deep-dive on TCIA + open imaging datasets

Pick one study from TCIA + open imaging datasets, reproduce the headline result, and write a short technical note on what you found.

Deliverable: GitHub repo + 3-page PDF write-up

Milestones

  • Run the official DICOM + 3D Slicer + Python tutorial end-to-end on real data
  • Download and explore one full dataset from TCIA + open imaging datasets
  • Document a clean QC + analysis pipeline that another person could rerun
Citation · course
BMJ Learning
BMJ
Why: Category match (Medicine & Healthcare)
Dataset · dataset
PubMed
NCBI
Why: Category match (Medicine & Healthcare)
3

Phase 3 — Applied projects

6–8 weeks 80–120 hours

Move from tutorials to original analyses on real questions. Start showing your work publicly.

Prerequisites

  • Phase 2 dataset deep-dive complete
  • Comfortable with the sector's primary tooling

Skills you'll build

End-to-end pipeline designStatistical interpretationScientific writingReproducible reports

Hands-on projects

Imaging informatics or AI project

Build an imaging analysis workflow on a TCIA dataset (e.g. lung nodule, breast) with appropriate validation.

Deliverable: Notebook + 5-page report

Milestones

  • Ship one original mini-analysis on a public dataset with clearly stated hypothesis and limitations
  • Engage with the RSNA and ESR radiology communities (post a question, answer one, or share a notebook)
  • Get peer feedback on at least one project and iterate
Citation · course
Coursera · Clinical Data Science
University of Colorado
Why: Category match (Medicine & Healthcare)
Dataset · dataset
PubMed
NCBI
Why: Category match (Medicine & Healthcare)
4

Phase 4 — Portfolio & career launch

3–5 weeks 30–50 hours

Package your work, target real roles, and prepare to interview in the sector.

Prerequisites

  • Phase 3 capstone project shipped
  • 2+ public repos under version control

Skills you'll build

Technical CVPortfolio siteInterview prep (case studies + technical questions)Networking

Hands-on projects

Job-ready portfolio package

Curate 2–3 of your strongest sector projects into a portfolio site with clear case-study writeups, plus a 1-page CV tailored to the target role.

Deliverable: Live portfolio URL + PDF CV + cover letter template

Milestones

  • Publish a portfolio site or pinned GitHub README linking to 2–3 projects
  • Tailor CV to 3 real job ads in the sector and submit applications
  • Practice 5 mock technical interviews with sector-specific case studies
Citation · training
NEJM Resident 360
NEJM
Why: Category match (Medicine & Healthcare)
Dataset · dataset
PubMed
NCBI
Why: Category match (Medicine & Healthcare)

Curated learning resources

Verified, canonical resources from the official providers in this sector. The AI roadmap builder draws from this same library when it personalizes your roadmap.

Courses

BMJ Learning
BMJ
Coursera · Clinical Data Science
University of Colorado
Python for Everybody
Coursera · Univ. of Michigan
LinkedIn Learning paths
LinkedIn

Trainings

NEJM Resident 360
NEJM
Software Carpentry lessons
The Carpentries
Kaggle Learn
Kaggle

Documentations

UpToDate clinical reference
Wolters Kluwer
Git & GitHub fundamentals
GitHub Docs

Datasets

PubMed
NCBI

Books

R for Data Science (2e)
Hadley Wickham et al.

Typical career paths

Radiologist

Interprets imaging and supports diagnosis and image-guided therapy.

Imaging Informatics Specialist

Designs PACS/AI workflows for imaging departments.

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