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Enterprise Imaging Webinar: Privacy and Patient Health Data #3 – Public Health and Big Data Perspectives
When: Apr 22, 2022 12:00 PM Eastern Time (US and Canada)
Healthcare providers, professionals and researchers are required to be good custodians of health information of patients. Our patients’ privacy must be protected. Paramount to the effective delivery of healthcare is the access to personal health information for those who need to know to provide care. Additionally, when collecting and using your personal health information measures must be in place to insure the privacy and confidentiality of the information is protected. The advent of Artificial Intelligence (AI) and Machine-Learning (ML) is showing that AI in Medical Imaging is requiring the “availability of high-quality image data for testing and validation. Open access data sharing is critical to enable adequate sampling of the human population and the wide variety of disease states. It is equally critical to protect patient privacy and to comply with national and international regulations governing protected or sensitive health information and personally identifiable information” (Data Sharing and Patient Privacy in the World of AI, Hot Topic session, RSNA 2021).
The topic of Privacy and Access to healthcare data has been introduced by members of our community frequently as the desired topic for our webinars. To suitably address this complex subject and the wide spectrum of perspectives, we are planning a series of three (3) one-hour webinars.
The third in this webinar series, Privacy and Patient Health Data – Public Health and Big Data Perspectives
- “Medical Data as a Public Good” – Dr. Matthew Lungren (MD, MPH), Co-Director Stanford Center for Artificial Intelligence in Medicine & Imaging
- Dr. Lungren is the Principal for Clinical AI/ML at AWS World Wide Public Sector Healthcare leading engagement for clinical machine learning collaborations and solutions for AWS partner organizations globally. Before joining AWS he was an Associate Professor and Radiologist at Stanford University where he led the creation of the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). He is an internationally recognized leader in clinical machine intelligence, in particular in computer vision in medical imaging applications, and has led the creation of dozens of applied artificial intelligence algorithms for medicine, including the first prospective multi-center clinical trial for AI in radiology. He has also led the curation of large open datasets at Stanford and via international partners to facilitate advancements of AI technologies in the global research and education communities. Dr. Lungren is also the co-creator of the most popular Coursera course on the fundamentals of machine learning in healthcare for healthcare practitioners achieving a top 1% instructor rating. His work has been featured in national news outlets such as NPR, Vice News, Scientific American and he regularly speaks at national and international scientific meetings on the topic of AI in healthcare.
- “Building Canada’s Health Data Foundation”– Eric Sutherland (M.Math, B.Math), Executive Director, pan-Canadian Health Data Strategy at Public Health Agency of Canada
- Eric is a “Data Guy with personality” and Data Strategist with a passion for enabling trusted insight through implementation of leading information management practices and linking enterprise strategy to practical solutions with business, technology, and data partners. He is the head of the Pan-Canadian Health Data Strategy which envisions the better use of data for health systems, public health, population health, research, and care. This work is led by the Public Health Agency of Canada in collaboration with all jurisdictions.
- Previously, Eric worked as Executive Director, Data Governance Strategy at the Canadian Institute for Health Information, and Director, Information Management Strategy and Policy at the Ontario Ministry of Health and Long-Term Care. He continues to recover from 20 years at a major Financial Institution, with a variety of roles lead data, business, and technology groups across Finance and Risk.
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