Meetings By Mail Decoding Radiology AI A SABI Primer 2025

Description

+ Include: 29 videos + 1 pdf, size: 2.44 GB

+ Target Audience: radiologists, imaging scientists, technologists

+ Sample video: contact me for sample video

+ 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.

 

+ 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