Adjunct Assoc Prof of Radiology, National Jewish Health. Senior Scientist, Data Science Institute at American College of Radiology Chair,Scientific Advisory Committee, Innosphere Ventures
Looking forward to this. To run AI for medical imaging reliably and safely at scale in a busy clinical practice will require robust systems, which no medical imaging clinical site has yet. I'll discuss some of the unique aspects of systems reliability engineering for medical imaging AI, suggest how we start to develop these systems, and look briefly at the more sophisticated systems we hope to evolve to. Almost no healthcare entity or radiology and pathology departments, or others who use medical images realizes they're absolutely going to need these systems as their clinical AI use increases in volume and sophistication. SIIM is excellently positioned to help educate people to become medical imaging systems reliability engineers. Just like PACS was the first clinical IT system in most hospitals, medical imaging will probably lead the way for medical AI systems reliability engineering, MBSE, and more.
Join the Closing Session of #SIIM24 for the 2024 Samuel J. Dwyer Lecture keynote from Raym Geis: Designing & Managing Medical Imaging Learning Systems Read about his experience at https://lnkd.in/eykZ66ZQ Jun 29 | National Harbor, MD Register Now to SAVE $100! https://lnkd.in/eAANwjVu Agenda | https://lnkd.in/eNz6zJH9
