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Explore the critical importance of context and specificity in AI research applications within healthcare through this 58-minute colloquium presented by Google Research Scientist Katherine Heller. Learn about methods for identifying and mitigating robustness and fairness issues in medical AI systems, with emphasis on human-centered approaches that ensure inclusive technology development. Discover how improved evaluations, out-of-distribution learning, and causal methods can help alleviate harms to underrepresented groups in healthcare AI. Examine real-world implementations including sepsis detection systems integrated into hospital emergency departments, surgical complication prediction tools, and mobile studies for Multiple Sclerosis research. Understand the development of globally sensitive data collection methods and their application to improving generative AI systems' understanding of healthcare contexts. Delve into innovative approaches for analyzing time series wearable data to predict mood disorders in maternal health settings. Gain insights into the intersection of foundational machine learning advances and pressing biomedical questions, presented as part of the joint colloquium series between the Eric and Wendy Schmidt Center at the Broad Institute and MIT's Department of Electrical Engineering and Computer Science.
