Attend the Clinical Translation Talk Series Webinar - Solving for X: Identifying Clinically Relevant Problems in Medical Imaging

Tuesday 10th September 2024

The MICCAI Clinical Translation Talk Series, organized by the Clinical Workgroup of the MICCAI Society, features talks by clinicians and researchers who are working at the intersection of research and clinical translation to highlight the problems and the lessons learned. Our first speaker in this series will be Dr. Tessa Cook from University of Pennsylvania.

Title: Solving for X: Identifying Clinically Relevant Problems in Medical Imaging

Speaker: Tessa Cook, MD PhD FSIIM FCPP, Associate Professor of Radiology, Vice Chair of Practice Transformation, University of Pennsylvania
Moderator: Charles Kahn, MD MS FACR, Professor of Radiology, Vice Chair of Informatics Radiology, University of Pennsylvania

Join us on September 24, 2024, 11:00 AM - 12:00 PM EDT / 5:00 PM - 6:00 PM CEST
Registration (required) is free and open to everyone.

About the speaker:

Dr. Tessa Cook is an Associate Professor of Radiology at the Perelman School of Medicine at the University of Pennsylvania in Philadelphia, and Vice Chair of Practice Transformation in the Department of Radiology. She is an active member of multiple radiology societies, including the RSNA, ACR, SIIM, and AUR. She is the director of the Imaging Informatics Fellowship and Modality Chief of 3-D and Advanced Imaging. Dr. Cook is the Past Chair of the Society for Imaging Informatics in Medicine (SIIM). In 2020, she was inducted into the College of SIIM Fellows and received SIIM’s Dr. Ruth Dayhoff Award for the Advancement of Women in Medical Imaging Informatics. She pursues innovative methods to enhance care delivery in radiology and improve radiologists' workflow. 

Abstract:

Medical image analysis has been a burgeoning field for decades. But only a fraction of the techniques developed cross over to the clinical workflow where they are used for patient care. Furthermore, most narrow AI currently focuses exclusively on pixel data as input and non-pixel data as labels. But in the real world, radiologists seamlessly integrate both into their interpretations and diagnoses. Shouldn't AI take consider the same set of input data as the human radiologist? In this webinar, we will discuss techniques for identifying clinically relevant and significant problems, as well as the importance of developing truly "multimodal" AI that meaningfully impacts care delivery.