Closing General Session – 2024 Samuel J. Dwyer Lecture
J. Raym Geis, MD, FSIIM
Adjunct Associate Professor of Radiology, National Jewish Health, Denver, CO
Session Number: 4022
Designing and Managing Medical Imaging Learning Systems
Explore the transformative possibilities of AI in advancing medical imaging beyond experimental phases. While AI holds promise in enhancing the speed, efficiency, and cost-effectiveness of medical imaging, its true potential emerges when integrated into new systems. Delve into the transition from limited machine learning experimentation to the extensive scaling of AI, envisioning intelligent radiology systems managing diverse functions across busy and dispersed clinical environments. This session will scrutinize the characteristics of these innovative systems, shedding light on the opportunities and challenges they pose for imaging informaticists, especially in high-stakes scenarios where precision is paramount.
To realize the value of incorporating intelligent tools into medical imaging will require sophisticated and entirely new workflows and systems engineering. SIIIM and its members are uniquely positioned to play huge roles in the development and operation of these systems."
Biography
Raym Geis MD is a radiologist interested in radiology data, standards, MLOps, and ethics of new data science approaches for medical imaging. He is Senior Scientist at the ACR Data Science Institute, Adjunct Associate Professor of Radiology at National Jewish Health, and Clinical Assistant Professor of Radiology at the University of Colorado School of Medicine. He chairs the Scientific Advisory Committee for Innosphere Ventures Fund , a Colorado-focused early stage VC fund. He is a member of the ACR Data Science Institute Advisory Group and the Canadian Association of Radiologists’ Artificial Intelligence Working Group, co-originator of the RSNA/SIIM National Imaging Informatics Curriculum and Course (NIIC-RAD), and honorary member of the European Society of Medical Imaging Informatics (EuSoMII). He was previously Chair of the Society for Imaging Informatics in Medicine (SIIM) and Vice Chair of the ACR Informatics Commission. He is a Fellow of SIIM and of the ACR.
This session is supported by Canon Medical through an unrestricted educational grant.
Objectives
- Recognize what new intelligent medical imaging systems and workflow may look like.
- Identify what imaging informaticists should start learning now to remain major participants in designing and operating these systems.
- Explore the transformative possibilities of AI in advancing medical imaging beyond experimental phases.
Credit Types
- ASRT-RT
- CAMPEP-MPCEC
- UUCME-MD
- SIIM IIP-CIIP
