The Statistical Methods for Health Equity Series is a monthly online series co-hosted by the Data Science for Health Equity community, the Alan Turing Institute Health Equity Interest Group, and the Department of Statistical Science at University College London.

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We are excited to announce the next instalment of our webinar series with speaker Sharon Davis (Vanderbilt University Medical Center)

Webinar Title: Supporting Responsible Deployment of AI in Healthcare with Sustainable and Generalizable Learning Prediction Systems

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Abstract: Successfully deploying impactful clinical AI tools is no small feat. Not only must we navigate clinical, technical, sociotechnical, and ethical challenges, but most critically, we ask patients and providers to trust and rely on these tools when making important health decisions. Such efforts compel us to be responsible stewards and ensure AI tools consistently perform as promised—overall, over time, across settings, and for all demographic, clinical, and geographic populations. Learning prediction systems, an extension of the learning health system paradigm, can enable generalizable and sustainable predictive AI tools and minimize disruptions resulting from deploying these tools across evolving clinical environments. We will explore novel approaches to localization and post-deployment maintenance of clinical prediction tools, including challenges and opportunities to fostering health and equity through model sustainability.

Bio: Sharon Davis is an Assistant Professor of Biomedical Informatics and the Associate Director of the Center for Improving the Public’s Health through Informatics at Vanderbilt University Medical Center. She is a biomedical informatician with a background in public health and statistics. Her current research emphasizes clinical predictive analytics, post-marketing surveillance, algorithmic vigilance, and responsible stewardship of artificial intelligence in healthcare. She is particularly interested in addressing the practical challenges of applied learning prediction systems, focusing on methods supporting the implementation of reliable, impactful, fair, and sustainable clinical AI models underlying tools for decision support and population management. The arc of Dr. Davis’ career and research portfolio are guided by a commitment to leveraging health and data sciences to advance tools that empower individuals, promote healthy communities, and reduce health inequities.

Please direct any questions about the webinar series to info@datascienceforhealthequity.com.