Description
+ Include: videos + file sub vtt + pdf, size: GB
+ Target Audience: radiologists, imaging scientists, and healthcare professionals who want to understand and apply AI tools in clinical radiology practice
+ Sample video: contact me for sample video
+ Information:
Clinical Artificial Intelligence in Radiology Categorical Course
Go from AI-curious to AI-confident.
This course cuts through the hype, giving you a practical playbook to understand, implement, and confidently collaborate with AI in your daily practice.
Learning Outcomes
As a result of attending this course, you will be able to:
- Analyze the basics of artificial intelligence and its value in radiology
- Create a practical framework for implementation of AI in clinical settings
- Describe radiology applications of AI and summarize research and education approaches
- Explain key perspectives of AI ethics, legal considerations, and human-centric AI
The American Roentgen Ray Society (ARRS) Clinical Artificial Intelligence in Radiology 2026 is best for radiologists, imaging scientists, and healthcare professionals who want to understand and apply AI tools in clinical radiology practice. It emphasizes practical integration of AI into workflows, evidence‑based evaluation of algorithms, and the future of imaging innovation.
👩⚕️ Target Audience
- Radiologists (general & subspecialty) seeking to integrate AI into diagnostic workflows.
- Residents & fellows in radiology preparing for board exams and future practice with AI.
- Medical imaging scientists & informaticians developing or validating AI applications.
- Healthcare administrators & IT leaders overseeing AI adoption in radiology departments.
- Advanced practice providers (NPs, PAs) collaborating in imaging‑driven patient care.
- Industry professionals & researchers working on AI solutions for medical imaging.
📚 What Participants Gain
- Clinical applications of AI: triage, detection, quantification, and workflow optimization.
- Validation & safety: understanding bias, reproducibility, and regulatory standards.
- Integration strategies: embedding AI into PACS, RIS, and clinical decision support.
- Future directions: generative AI, multimodal imaging, and personalized diagnostics.
- Case‑based learning: real examples of AI in radiology practice.
+ Topics:
| Sunday, April 12, 2026 | |
|---|---|
| 10:00 am–12:00 pm: Getting to Know AI | |
| Overview of Radiology Artificial Intelligence: Latest Progress | Tessa Cook, MD, PhD |
| Primer on Artificial Intelligence (AI): Deep Learning, Natural Language Processing and Large Language Models, Generative AI, Agentic AI, and Radiomics | Hyun Soo Ko, MD |
| Artificial Intelligence Can Improve Radiology Workflow Efficiency By Automating Noninterpretive Tasks | Linda Moy, MD |
| Artificial Intelligence to Improve Radiology Imaging Interpretation | Shandong Wu, PhD |
| 1:00 pm – 3:00 pm: AI Clinical Implementation | |
| Legal and Ethical Considerations in AI Implementation | Julian Rivera, JD |
| Artificial Intelligence Deployment | Tessa Cook, MD, PhD |
| Artificial Intelligence (AI) Regulation and Governance: A Practice Perspective On How to Govern Assessment, Deployment, and Maintenance of AI Algorithms | Melissa Davis, MD, MBA |
| Panel Discussion | Linda Moy, MD (Moderator); Julian Rivera, JD; Tessa Cook, MD, PhD; Melissa Davis, MD, MBA |
| 3:30 pm – 5:30 pm: Going Beyond Images to Multimodality | |
| Medical Imaging Dataset Curation for Artificial Intelligence | Heather Whitney, PhD |
| Multimodal Foundation Models in Radiology | Christian Bluethgen, MD |
| Physics and Artificial Intelligence in CT | Lifeng Yu, PhD |
| Panel Discussion | Heather Whitney, PhD; Christian Bluethgen, MD; Lifeng Yu, PhD |
| Monday, April 13, 2026 | |
|---|---|
| 7:30 am – 9:30 am: AI Use Cases in Subspecialties: Breast, Neuro, Abdominal, and MSK | |
| Breast Imaging Artificial Intelligence | Constance Lehman, MD, PhD |
| Artificial Intelligence in Neuroradiology | Paulo Kuriki, MD |
| Artificial Intelligence in Abdominal Imaging | Yee Ng, MD |
| Current and Emerging Applications of AI in Musculoskeletal Imaging | Ali Guermazi, MD |
| 10:00 am – 12:00 pm: AI Use Cases in Subspecialties: Peds, Cardiothoracic, IR, NM | |
| Pediatric Radiology Artificial Intelligence | Edward Lee, MD, MPH |
| Artificial Intelligence in Cardiothoracic Imaging: From Decision Support to Prognostic Biomarkers | Fernando Kay, MD |
| Applications of Artificial Intelligence in Interventional Radiology | Satvik Tripathi |
| Nuclear Medicine AI | Babak Saboury, MD |
| 1:00 pm – 3:00 pm: AI Research and Education | |
| Demonstration of an Artificial Intelligence Model Development Process (From A to Z) in Radiology | Dooman Arefan, PhD |
| Artificial Intelligence Research in Radiology: Team, Approach, and Direction | Shandong Wu, PhD |
| Clinically Fluent, AI Literate: Teaching Radiologists About and With AI | Justin Peacock, MD, PhD |
| Panel Discussion | Shandong Wu, PhD (Moderator); Dooman Arefan, PhD; Justin Peacock, MD, PhD |
| 3:30 pm – 5:30 pm: Humanity and AI | |
| Radiologist-Artificial Intelligence Collaboration and Teaming | Florence Doo, MD |
| Bias and Fairness of Artificial Intelligence in Radiology: Current State and Future Directions | Judy Gichoya, MD, MS |
| Radiologists Fit in AI-Powered Radiology Services That Are Radiologist-Centered | Eduardo Barbosa, MD, MBA |
| Panel Discussion | Charles Kahn, Jr., MD, MS (Moderator); Florence Doo, MD; Judy Gichoya, MD, MS; Eduardo Barbosa, MD, MBA |
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