{"product_id":"meetings-by-mail-decoding-radiology-ai-a-sabi-primer-2025","title":"Meetings By Mail Decoding Radiology AI A SABI Primer 2025","description":"\u003cul\u003e\n  \u003cli\u003eTarget Audience: radiologists, imaging scientists, technologists\u003c\/li\u003e\n  \u003cli\u003eSample video: contact me for sample video\u003c\/li\u003e\n  \u003cli\u003eInformation:\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eRelease date: October 21, 2025\u003c\/p\u003e\n\u003cp\u003eA 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.\u003c\/p\u003e\n\u003cp\u003eTopics 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!\u003c\/p\u003e\n\u003cp\u003eThe 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.\u003c\/p\u003e\n\u003cp\u003eWho Should Enroll\u003c\/p\u003e\n\u003cp\u003eRadiologists \u0026amp; imaging specialists seeking to understand and apply AI in diagnostic imaging.\u003c\/p\u003e\n\u003cp\u003eMedical physicists \u0026amp; imaging scientists exploring algorithm development and validation.\u003c\/p\u003e\n\u003cp\u003eRadiology technologists interested in how AI tools affect workflow and image acquisition.\u003c\/p\u003e\n\u003cp\u003eHealthcare administrators \u0026amp; leaders evaluating AI adoption in radiology departments.\u003c\/p\u003e\n\u003cp\u003eResidents, fellows, and trainees in radiology who need structured exposure to AI concepts.\u003c\/p\u003e\n\u003cp\u003eIndustry professionals (AI developers, vendors) wanting to align solutions with clinical needs.\u003c\/p\u003e\n\u003cp\u003eWhat You’ll Learn\u003c\/p\u003e\n\u003cp\u003eAI fundamentals in radiology: machine learning, deep learning, and neural networks explained.\u003c\/p\u003e\n\u003cp\u003eClinical applications: AI in image interpretation, workflow optimization, and decision support.\u003c\/p\u003e\n\u003cp\u003eValidation \u0026amp; regulation: FDA approval pathways, bias reduction, and ethical considerations.\u003c\/p\u003e\n\u003cp\u003eCase‑based examples: real‑world demonstrations of AI tools in radiology practice.\u003c\/p\u003e\n\u003cp\u003eFuture directions: generative AI, multimodal imaging integration, and precision medicine.\u003c\/p\u003e\n\u003cul\u003e\n  \u003cli\u003eTopics:\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eSession 1: AI Fundamentals\u003c\/p\u003e\n\u003cp\u003eIntroduction to AI Terms and Methods\u003c\/p\u003e\n\u003cp\u003eJordan Perchik, MD\u003c\/p\u003e\n\u003cp\u003eAI and the User Interface\u003c\/p\u003e\n\u003cp\u003eDr. Clare Rainey\u003c\/p\u003e\n\u003cp\u003eQ\u0026amp;A: Artificial Intelligence You Need to Know\u003c\/p\u003e\n\u003cp\u003eMultiple Faculty\u003c\/p\u003e\n\u003cp\u003eRadiomics in Pancreatic Tumor Imaging\u003c\/p\u003e\n\u003cp\u003eRichard Do, MD, PhD\u003c\/p\u003e\n\u003cp\u003eLarge Language Models Integration Into Radiology Workflow: Potential Applications, Efficacy and Limitations\u003c\/p\u003e\n\u003cp\u003eSoheil Kooraki, MD\u003c\/p\u003e\n\u003cp\u003eImproving Radiology Report Conciseness \u0026amp; Structure Via Large Language Models\u003c\/p\u003e\n\u003cp\u003eLes Folio, DO, MPH\u003c\/p\u003e\n\u003cp\u003eSession 2: Body Imaging Applications\u003c\/p\u003e\n\u003cp\u003eIntelligent Scanning: Using Automated Tools to Improve and Personalize MRI Scans\u003c\/p\u003e\n\u003cp\u003eAngela Tong, MD\u003c\/p\u003e\n\u003cp\u003eAI in Pulmonary Imaging\u003c\/p\u003e\n\u003cp\u003eSteven Rothenberg, MD\u003c\/p\u003e\n\u003cp\u003eApplication of AI in Cardiac Imaging\u003c\/p\u003e\n\u003cp\u003eHuma Samar, MBBS\u003c\/p\u003e\n\u003cp\u003eDeep Learning Advances in Cardiopulmonary Imaging– Flow, Structure and Function\u003c\/p\u003e\n\u003cp\u003eAlbert Hsiao, MD, PhD\u003c\/p\u003e\n\u003cp\u003eAbdominal Organ Segmentations: The Power of Deep Learning\u003c\/p\u003e\n\u003cp\u003eMartin Prince, MD, PhD\u003c\/p\u003e\n\u003cp\u003eMy Favorite App For That\u003c\/p\u003e\n\u003cp\u003eJordan Perchik, MD\u003c\/p\u003e\n\u003cp\u003eHot Topics in AI: Updates in Pancreatic Imaging\u003c\/p\u003e\n\u003cp\u003eLinda Chu, MD\u003c\/p\u003e\n\u003cp\u003eHot Topics in AI: Enhancing Endometriosis Detection: A Deep Learning Approach Using MRI Imaging\u003c\/p\u003e\n\u003cp\u003eMana Moassefi, MD\u003c\/p\u003e\n\u003cp\u003eSession 3:  Imaging Applications for MSK, Neuro, Breast \u0026amp; More\u003c\/p\u003e\n\u003cp\u003eAI in MSK Imaging\u003c\/p\u003e\n\u003cp\u003eJake Mandell, MD\u003c\/p\u003e\n\u003cp\u003eAI Applications in Neuroradiology\u003c\/p\u003e\n\u003cp\u003eMarwa Ismail, PhD\u003c\/p\u003e\n\u003cp\u003eAI in Pediatric Imaging\u003c\/p\u003e\n\u003cp\u003eAndrew Smith, MD, PhD\u003c\/p\u003e\n\u003cp\u003eIntroduction to AI Applications in Breast Imaging\u003c\/p\u003e\n\u003cp\u003eMark Traill, MD\u003c\/p\u003e\n\u003cp\u003eAI in Nuclear Imaging\u003c\/p\u003e\n\u003cp\u003eTyler Bradshaw, PhD\u003c\/p\u003e\n\u003cp\u003eSession 4: Ethics and Bias\u003c\/p\u003e\n\u003cp\u003eIntelligent Imaging: Exploring the Sustainability of Radiology and Radiology AI\u003c\/p\u003e\n\u003cp\u003eFlorence Doo, MD\u003c\/p\u003e\n\u003cp\u003eNavigating Bias in Artificial Intelligence for Clinical Radiology: Key Considerations and Challenges\u003c\/p\u003e\n\u003cp\u003eMelina Hosseiny, MD and Rita Maria Lahoud, MD\u003c\/p\u003e\n\u003cp\u003eEthical Considerations in Imaging AI\u003c\/p\u003e\n\u003cp\u003eMuhammad Umair, MD\u003c\/p\u003e\n\u003cp\u003eBias in AI: Case Study in Comparing Performance Between US and African Hospitals\u003c\/p\u003e\n\u003cp\u003eJordan Perchik, MD\u003c\/p\u003e\n\u003cp\u003ePanel Discussion: Should We Let Computers Write Our Reports for Us?\u003c\/p\u003e\n\u003cp\u003eMultiple Faculty\u003c\/p\u003e\n\u003cp\u003eSession 5: Looking Forward . . .\u003c\/p\u003e\n\u003cp\u003ePreparing Radiologists for an AI Enhanced Future: Practical Tips for Trainees\u003c\/p\u003e\n\u003cp\u003eMelina Hosseiny, MD\u003c\/p\u003e\n\u003cp\u003eAI and Its Changing Role in Healthcare\u003c\/p\u003e\n\u003cp\u003eOmer Awan, University of Maryland\u003c\/p\u003e\n\u003cp\u003eUpdate on AI and Academic Publishing\u003c\/p\u003e\n\u003cp\u003eEric Tamm, MD, Ali Shah Tejani, MD and Samuel Galgano, MD\u003c\/p\u003e\n\u003cp\u003eChatGPT \u0026amp; Generative AI: A New Frontier For Healthcare\u003c\/p\u003e\n\u003cp\u003eFlorence Doo, MD\u003c\/p\u003e\n\u003cp\u003ePanel Discussion: Generative AI\u003c\/p\u003e\n\u003cp\u003eMultiple Faculty\u003c\/p\u003e","brand":"Medicalcoursespro","offers":[{"title":"Default Title","offer_id":46405800952004,"sku":null,"price":39.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0751\/7644\/4100\/files\/Meetings-By-Mail-Decoding-Radiology-AI-A-SABI-Primer-2025.jpg?v=1784216370","url":"https:\/\/medicalcoursespro.com\/products\/meetings-by-mail-decoding-radiology-ai-a-sabi-primer-2025","provider":"Medicalcoursespro","version":"1.0","type":"link"}