Temerty Centre Speaker Series: AI Deployment in Medicine
Temerty Centre Speaker Series: AI Deployment in Medicine
Date: January 11, 2022 (Tuesday)
Time: 12pm to 1pm
Method: Zoom
Note to physicians: This event is CPD accredited
This event has already taken place. To view the recording of this event, visit our Past Events page.
AI Deployment in Medicine
AI applications across a wide range of specialties are beginning to transition from research papers into impact at the point of care. Deploying AI successfully and responsibly involves considering the technical and sociological impacts these tools will have in the clinical environment. Drs. Adedinsewo, Berlin, and Liu have developed and are working to deploy algorithms across cardiology, radiation oncology, and critical care. T-CAIREM is thrilled to have them kick off our 2022 Speaker Series by sharing their lessons learned across the AI development pipeline to ultimately get AI innovations to the patients that may benefit most from them.
Dr. Demilade Adedinsewo
MD, MPH, FACC
Assistant Professor of Medicine, Mayo Clinic College of Medicine and Science
Senior Associate Consultant, Division of Cardiovascular Medicine
Dr. Demilade Adedinsewo is an Assistant Professor of Medicine and non-invasive cardiologist at Mayo Clinic in Florida with a clinical focus on women’s heart health and echocardiography. She is a fellow of the American College of Cardiology and her research interests include cardiovascular epidemiology, cardiovascular disease in women, and the applications of artificial intelligence (AI) tools in cardiovascular disease detection. She is currently a Women’s Health Scholar and her research is supported by the Mayo Clinic Women’s Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women’s Health (BIRCWH) Program funded by the National Institutes of Health.
She received her medical degree from Obafemi Awolowo University, Ile-Ife, Nigeria; a Master of Public Health degree from Emory University in Atlanta; completed internal medicine residency at Morehouse School of Medicine and cardiovascular disease fellowship at Mayo Clinic, Florida. She worked at the Centers for Disease Control and Prevention (CDC) as a research fellow and epidemiologist prior to her residency training and completed a professional certificate degree in Machine Learning and Artificial Intelligence at the Massachusetts Institute of Technology.
Dr. Alejandro Berlin
Clinician-Scientist | Radiation Oncologist | Princess Margaret Cancer Centre
UHN Assistant Professor | Department of Radiation Oncology | UofT
Affiliated Faculty | Techna Institute | UHN
Associate Member (PAC) | IMS | UofT
Dr. Alejandro (Ale) Berlin is a staff clinician-scientist and radiation oncologist at The Princess Margaret (PM), as well as the Medical Director of the Cancer Digital Intelligence Pillar. He obtained his Medical Degree as valedictorian from the Pontifical Catholic University of Chile and underwent radiation oncology specialty training in Chile, Israel and Canada.
Dr. Berlin's clinical practice and research focus on the characterization of genitourinary malignancies with molecular and MR imaging, and novel applications of curative treatments combining systemic, stereotactic radiotherapy and MR-guided brachytherapy approaches. Dr. Berlin is particularly interested in the design of innovative clinical trials and the discovery of biomarkers using high-throughput genomics that may ultimately translate into novel individualized treatments for patients with prostate cancer. Dr. Berlin leads the clinical deployment of several novel technologies, including PM virtual care efforts, a unique pilot deploying an in-house developed e-platform to support a new model of care (i.e., asynchronous visits) for prostate cancer survivors, and the application of artificial intelligence-based methods for radiotherapy planning.
Dr. Vincent Liu
Research Scientist II, Kaiser Permanente Northern California, Division of Research
Regional Director, Hospital Advanced Analytics, The Permanente Medical Group
Dr. Vincent Liu is a Research Scientist at the Kaiser Permanente Division of Research and an Associate Professor of Health Systems Science at the KP School of Medicine. He is the Regional Director for Hospital Advanced Analytics, a role in which he oversees the use of real-time clinical predictive models in the KP Northern California health system serving 4.5 million members across 21 hospitals. His research focuses on the intersection of acute illness and sepsis, machine learning, and program evaluation to identify effective treatments to improve acute care outcomes.
Learning objectives
• Understand the necessary validation steps to take before an AI algorithm is ready for a prospective validation study (i.e. a silent trial)
• Outline key considerations when designing a prospective validation study for an AI algorithm
• Recognize the barriers and enablers of launching an AI deployment (e.g. institutional buy-in, integration with the EMR, etc.)
