Go from AI-Curious to AI-Confident
Clinical Artificial Intelligence in Radiology Categorical Course
A practical, clinically focused course designed to cut through the hype and help radiology professionals understand, implement, evaluate, and collaborate with AI in daily practice.
A practical playbook for AI in modern radiology
This course moves from core AI concepts to real-world clinical deployment, helping learners understand the technologies behind modern radiology AI and how they fit into workflow, interpretation, research, education, and governance.
Content spans deep learning, NLP, large language models, generative and agentic AI, radiomics, multimodal foundation models, workflow automation, subspecialty use cases, legal and ethical considerations, model development, bias, fairness, and radiologist-AI collaboration.
- Understand the core language and concepts behind modern AI
- Build a practical clinical implementation framework
- Review AI use cases across major radiology subspecialties
- Explore model development, research, and education
- Address ethics, regulation, bias, fairness, and human-centered AI
Four Core Areas of AI Readiness
Understand AI
Deep learning, NLP, LLMs, generative AI, agentic AI, radiomics, and multimodal models.
Implement AI
Deployment, workflow integration, governance, algorithm assessment, and maintenance.
Apply AI
Breast, neuro, abdominal, MSK, pediatric, cardiothoracic, IR, and nuclear medicine use cases.
Lead Responsibly
Ethics, legal issues, fairness, bias, human-AI teaming, and radiologist-centered services.
What You Will Learn
Course Directors
Shandong Wu, PhD
Course DirectorUniversity of Pittsburgh
Yee Seng Ng, MD
Course DirectorUniversity of Washington
Hyun Soo Ko, MD
Course DirectorPeter MacCallum Cancer Centre
2-Day Clinical AI in Radiology Agenda
28 sessions across 7 program blocks.
Sunday, April 12, 2026 12 sessions
Getting to Know AI
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SessionTessa Cook, MD, PhD
Overview of Radiology Artificial Intelligence: Latest Progress
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SessionHyun Soo Ko, MD
Primer on Artificial Intelligence (AI): Deep Learning, Natural Language Processing and Large Language Models, Generative AI, Agentic AI, and Radiomics
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SessionLinda Moy, MD
Artificial Intelligence Can Improve Radiology Workflow Efficiency By Automating Noninterpretive Tasks
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SessionShandong Wu, PhD
Artificial Intelligence to Improve Radiology Imaging Interpretation
AI Clinical Implementation
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SessionJulian Rivera, JD
Legal and Ethical Considerations in AI Implementation
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SessionTessa Cook, MD, PhD
Artificial Intelligence Deployment
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SessionMelissa Davis, MD, MBA
Artificial Intelligence (AI) Regulation and Governance: A Practice Perspective On How to Govern Assessment, Deployment, and Maintenance of AI Algorithms
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SessionLinda Moy, MD (Moderator); Julian Rivera, JD; Tessa Cook, MD, PhD; Melissa Davis, MD, MBA
Panel Discussion
Going Beyond Images to Multimodality
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SessionHeather Whitney, PhD
Medical Imaging Dataset Curation for Artificial Intelligence
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SessionChristian Bluethgen, MD
Multimodal Foundation Models in Radiology
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SessionLifeng Yu, PhD
Physics and Artificial Intelligence in CT
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SessionHeather Whitney, PhD; Christian Bluethgen, MD; Lifeng Yu, PhD
Panel Discussion
Monday, April 13, 2026 16 sessions
AI Use Cases in Subspecialties: Breast, Neuro, Abdominal, and MSK
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SessionConstance Lehman, MD, PhD
Breast Imaging Artificial Intelligence
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SessionPaulo Kuriki, MD
Artificial Intelligence in Neuroradiology
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SessionYee Ng, MD
Artificial Intelligence in Abdominal Imaging
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SessionAli Guermazi, MD
Current and Emerging Applications of AI in Musculoskeletal Imaging
AI Use Cases in Subspecialties: Peds, Cardiothoracic, IR, NM
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SessionEdward Lee, MD, MPH
Pediatric Radiology Artificial Intelligence
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SessionFernando Kay, MD
Artificial Intelligence in Cardiothoracic Imaging: From Decision Support to Prognostic Biomarkers
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SessionSatvik Tripathi
Applications of Artificial Intelligence in Interventional Radiology
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SessionBabak Saboury, MD
Nuclear Medicine AI
AI Research and Education
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SessionDooman Arefan, PhD
Demonstration of an Artificial Intelligence Model Development Process (From A to Z) in Radiology
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SessionShandong Wu, PhD
Artificial Intelligence Research in Radiology: Team, Approach, and Direction
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SessionJustin Peacock, MD, PhD
Clinically Fluent, AI Literate: Teaching Radiologists About and With AI
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SessionShandong Wu, PhD (Moderator); Dooman Arefan, PhD; Justin Peacock, MD, PhD
Panel Discussion
Humanity and AI
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SessionFlorence Doo, MD
Radiologist-Artificial Intelligence Collaboration and Teaming
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SessionJudy Gichoya, MD, MS
Bias and Fairness of Artificial Intelligence in Radiology: Current State and Future Directions
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SessionEduardo Barbosa, MD, MBA
Radiologists Fit in AI-Powered Radiology Services That Are Radiologist-Centered
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SessionCharles Kahn, Jr., MD, MS (Moderator); Florence Doo, MD; Judy Gichoya, MD, MS; Eduardo Barbosa, MD, MBA
Panel Discussion
Expert Faculty
Faculty listed across implementation, subspecialty AI, research, education, ethics, governance, and human-AI collaboration.
Tessa Cook, MD, PhD
Hyun Soo Ko, MD
Linda Moy, MD
Shandong Wu, PhD
Julian Rivera, JD
Melissa Davis, MD, MBA
Heather Whitney, PhD
Christian Bluethgen, MD
Lifeng Yu, PhD
Constance Lehman, MD, PhD
Paulo Kuriki, MD
Yee Ng, MD
Ali Guermazi, MD
Edward Lee, MD, MPH
Fernando Kay, MD
Satvik Tripathi
Babak Saboury, MD
Dooman Arefan, PhD
Justin Peacock, MD, PhD
Florence Doo, MD
Judy Gichoya, MD, MS
Eduardo Barbosa, MD, MBA
Charles Kahn, Jr., MD, MS
Move from AI curiosity to practical clinical confidence
Review modern AI concepts, implementation, multimodal models, workflow automation, subspecialty applications, model development, ethics, regulation, bias, and radiologist-AI collaboration in one focused 2026 course.
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