Meetings By Mail Decoding Radiology AI A SABI Primer 2025

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.

 
+ Topics:

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