Meetings By Mail Decoding Radiology AI A SABI Primer 2025
+ Include: 29 videos + 1 pdf, size: 2.44 GB + Target Audience: radiologists, imaging scientists, technologists + Information: Release date: October 21, 2025 A source of innovation for nearly fifty years, the Society for Advanced Body Imaging (SABI) presents this exploration into AI fundamentals, practical uses and opportunities for Radiologists at all experience levels. Decoding Radiology AI: A SABI Primer provides the requisite foundational skills and perspectives to flourish in this constantly evolving technological landscape. Topics include: Fundamentals, radiomics, large language model integration, reporting, applications for body, cardiothoracic, MSK, neuro, pediatric, breast and nuclear imaging, ethics and bias, generative AI and much more! The Meetings By Mail: Decoding Radiology AI – A SABI Primer 2025 is best for radiologists, imaging scientists, technologists, and healthcare leaders who want training on how artificial intelligence is reshaping radiology practice. It is designed as a flexible, on‑demand program that introduces AI concepts from fundamentals to clinical applications, with a focus on practical integration into imaging workflows.Who Should Enroll
- Radiologists & imaging specialists seeking to understand and apply AI in diagnostic imaging.
- Medical physicists & imaging scientists exploring algorithm development and validation.
- Radiology technologists interested in how AI tools affect workflow and image acquisition.
- Healthcare administrators & leaders evaluating AI adoption in radiology departments.
- Residents, fellows, and trainees in radiology who need structured exposure to AI concepts.
- Industry professionals (AI developers, vendors) wanting to align solutions with clinical needs.
What You’ll Learn
- AI fundamentals in radiology: machine learning, deep learning, and neural networks explained.
- Clinical applications: AI in image interpretation, workflow optimization, and decision support.
- Validation & regulation: FDA approval pathways, bias reduction, and ethical considerations.
- Case‑based examples: real‑world demonstrations of AI tools in radiology practice.
- Future directions: generative AI, multimodal imaging integration, and precision medicine.
| Session 1: AI Fundamentals | |
| Introduction to AI Terms and Methods | Jordan Perchik, MD |
| AI and the User Interface | Dr. Clare Rainey |
| Q&A: Artificial Intelligence You Need to Know | Multiple Faculty |
| Radiomics in Pancreatic Tumor Imaging | Richard Do, MD, PhD |
| Large Language Models Integration Into Radiology Workflow: Potential Applications, Efficacy and Limitations | Soheil Kooraki, MD |
| Improving Radiology Report Conciseness & Structure Via Large Language Models | Les Folio, DO, MPH |
| Session 2: Body Imaging Applications | |
| Intelligent Scanning: Using Automated Tools to Improve and Personalize MRI Scans | Angela Tong, MD |
| AI in Pulmonary Imaging | Steven Rothenberg, MD |
| Application of AI in Cardiac Imaging | Huma Samar, MBBS |
| Deep Learning Advances in Cardiopulmonary Imaging– Flow, Structure and Function | Albert Hsiao, MD, PhD |
| Abdominal Organ Segmentations: The Power of Deep Learning | Martin Prince, MD, PhD |
| My Favorite App For That | Jordan Perchik, MD |
| Hot Topics in AI: Updates in Pancreatic Imaging | Linda Chu, MD |
| Hot Topics in AI: Enhancing Endometriosis Detection: A Deep Learning Approach Using MRI Imaging | Mana Moassefi, MD |
| Session 3: Imaging Applications for MSK, Neuro, Breast & More | |
| AI in MSK Imaging | Jake Mandell, MD |
| AI Applications in Neuroradiology | Marwa Ismail, PhD |
| AI in Pediatric Imaging | Andrew Smith, MD, PhD |
| Introduction to AI Applications in Breast Imaging | Mark Traill, MD |
| AI in Nuclear Imaging | Tyler Bradshaw, PhD |
| Session 4: Ethics and Bias | |
| Intelligent Imaging: Exploring the Sustainability of Radiology and Radiology AI | Florence Doo, MD |
| Navigating Bias in Artificial Intelligence for Clinical Radiology: Key Considerations and Challenges | Melina Hosseiny, MD and Rita Maria Lahoud, MD |
| Ethical Considerations in Imaging AI | Muhammad Umair, MD |
| Bias in AI: Case Study in Comparing Performance Between US and African Hospitals | Jordan Perchik, MD |
| Panel Discussion: Should We Let Computers Write Our Reports for Us? | Multiple Faculty |
| Session 5: Looking Forward . . . | |
| Preparing Radiologists for an AI Enhanced Future: Practical Tips for Trainees | Melina Hosseiny, MD |
| AI and Its Changing Role in Healthcare | Omer Awan, University of Maryland |
| Update on AI and Academic Publishing | Eric Tamm, MD, Ali Shah Tejani, MD and Samuel Galgano, MD |
| ChatGPT & Generative AI: A New Frontier For Healthcare | Florence Doo, MD |
| Panel Discussion: Generative AI | Multiple Faculty |
