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Overview

Creating a collaborative ecosystem and robust infrastructure to enable AI to improve the field of Radiology and improve patient outcomes.
Artificial Intelligence has the power to truly transform radiological practice. The benefits of machine learning and deep neural networks in identifying disease earlier and more accurately is irrefutable. The integration of this technology at scale is, however, more challenging. How do we invest in building the necessary frameworks to validate these tools in the clinical setting? How do we ensure we are working in unison as an industry and not in a siloed and duplicative fashion. How do we ensure we maintain physician oversight/domain expertise when these tools are being developed?

Topics to be discussed will include

  • The need for unbiased Ground Truth Data
  • Overhaul of IT infrastructure within the healthcare system at large.
  • Patient privacy/protection issues
  • Develop comprehensive partnerships between clinicians with the vendor community to develop tools of clinical utility and usability
  • How do we speed up research without putting scientific integrity at risk. Is there a hybrid model?

 

Presenters

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Don Dennison

Medical Imaging Informatics Consultant

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Don Dennison is a consultant that has worked in the medical imaging informatics industry for over 15 years. A Fellow of the College of the Society of Imaging Informatics in Medicine (SIIM), he is a frequent speaker and panelist at SIIM, RSNA, and other conferences on topics ranging from medical imaging record interoperability, integration of imaging data within the EMR, multi-facility integration, and others. Don has published several articles and eBooks on patient identity management, IT strategies for consolidated enterprises, as well as VNAs, PACS, and Enterprise Viewers. Don currently serves as Chair or a member of several imaging informatics society committees and recently joined the Journal of Digital Imaging (JDI) editorial board. Don Co-chairs MIIT, an annual imaging informatics education conference in Canada.

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Anna Goldenberg

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Dr Goldenberg is a Senior Scientist in Genetics and Genome Biology program at SickKids Research Institute, recently appointed as the first Varma Family Chair in Biomedical Informatics and Artificial Intelligence. She is also an Associate Professor in the Department of Computer Science at the University of Toronto, faculty member and an Associate Research Director, Health at Vector Institute and a fellow at the Canadian Institute for Advanced Research (CIFAR), Child and Brain Development group. Dr Goldenberg trained in machine learning at Carnegie Mellon University, with a post-doctoral focus in computational biology and medicine. The current focus of her lab is on developing machine learning methods that capture heterogeneity and identify disease mechanisms in complex human diseases as well as developing risk prediction and early warning clinical systems. Dr Goldenberg is a recipient of the Early Researcher Award from the Ministry of Research and Innovation and a Canada Research Chair in Computational Medicine. She is strongly committed to creating responsible AI to benefit patients across a variety of conditions.

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April Khademi

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Dr. Khademi is Assistant Professor of Biomedical Engineering at Ryerson University and PI of the Image Analysis in Medicine Lab (IAMLAB), which specializes in the design of image analysis and machine learning (AI) algorithms for medical imaging. She is an Investigator for the Canadian Consortium of Neurodegeneration and Aging (CCNA) and Affiliate Scientist at St. Michael’s Hospital and the Institute for Biomedical Engineering, Science & Technology (iBEST). April holds a Ph.D. degree in Electrical Engineering from University of Toronto where she developed novel segmentation and quantification algorithms for neurological MRI. She has had previous roles in medical imaging algorithm development at the University of Guelph, GE Healthcare/Omnyx, Pathcore Inc., Sunnybrook Research Institute and Toronto Rehab Institute. Through April’s academic and industrial experiences, she is trained in designing commercial AI algorithms for medical imaging and is committed to developing tools that are clinically translatable. April uses an interdisciplinary design philosophy grounded in physics, medicine, engineering and computer science to maximize translation opportunities. Dr. Khademi has complemented her technical expertise with a portfolio of patents, publications, speaking engagements and editorial activities. She is a licensed Professional Engineer in Ontario and IEEE Senior Member. More information: www.ryerson.ca/akhademi

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Errol Colak

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Dr. Errol Colak is the Clinical Lead of the Diagnostic Imaging and Learning Algorithms (DILA) group at The Li Ka Shing Centre for Healthcare Analytics Research & Training. He is a staff radiologist and Director of Body CT in the Department of Medical Imaging of St. Michael’s Hospital and Assistant Professor at the University of Toronto. His research interests include machine learning, pelvic floor disorders, bowel imaging, and genitourinary malignancies.

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